The article presents several innovative systems developed through student laboratory projects, including two autonomous vehicles, a four-legged walking robot, an active ankle-foot orthosis, a ball on beam balancing mechanism, a ball on plate system, and a manipulator arm—all powered by pneumatic artificial muscles (PAMs). Fluidic muscles offer significant potential in various mechatronic systems, robots, and manipulators due to their flexible, lightweight nature and compliance. Their ability to produce smooth and adaptable motions is particularly beneficial in applications requiring natural and human-like movement, such as rehabilitation and assistive devices. Although PAMs powered control systems face challenges with nonlinear phenomena, their biologically inspired design holds promise for future applications across various industries. These may include innovative applicability in industrial, automotive, and aerospace sectors, as well as use in sports, medical aids, entertainment, and animatronics. Incorporating self-made laboratory systems actuated by PAMs into control systems education offers a comprehensive learning opportunity that combines theoretical concepts with hands-on experience. This approach develops the necessary skills of future engineers, expanding their understanding of technical principles and preparing them for future challenges in engineering practice.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

FPGA-based data acquisition and processing systems play an important role in modern high-speed, multichannel measurement systems, especially in High-Energy and Plasma Physics. Such FPGA-based systems require an extended control and diagnostics part corresponding to the complexity of the controlled system. Managing the complex structure of registers while keeping the tight coupling between hardware and software is a tedious and potentially error-prone process. Various existing solutions aimed at helping that task do not perfectly match all specific requirements of that application area. The paper presents a new solution based on the XML system description, facilitating the automated generation of the control system’s HDL code and software components and enabling easy integration with the control software. The emphasis is put on reusability, ease of maintenance in case of system modification, easy detection of mistakes, and the possibility of use in modern FPGAs. The presented system has been successfully used in data acquisition and preprocessing projects in High-Energy Physics experiments. It enables easy creation and modification of the control system definition and convenient access to the control and diagnostic blocks. The presented system is an open-source solution and may be adopted by the user for particular needs.

Food safety and quality are the first steps in the food chain. This work reports a low-cost and easy-to-use optoelectronic measurement system for improving feed chain quality and safety, based on near-infrared spectrometry (NIRS) technology. This is a significant challenge for dairy farm technicians and producers who need rapid and reliable knowledge of forage quality on their farms. In most cases, instrumentation suitable for these specifications is expensive and difficult to operate. The core of the measurement system is Texas Instruments´ NIRscan Nano Evaluation Module (EVM) spectrometer. This module has a large sensing area and high resolution suitable for forage samples. To evaluate the feasibility of the prototype to analyze agrifood samples, different ways of presenting the sample, intact or ground, were tested. The final objective of the research is not just to check the efficiency of the proposed system. It is also to determine the measurement system characteristics and how to improve them.

The autonomous displacement and displacement measurement functions of the maglev ruler are performed by the mover core. The stability of the four magnetic field in air gaps and the levitation position of the maglev ruler is one of the key factors for the stability of the thrust force on the power core, and it is also one of the key factors for ensuring the precision of the maglev ruler. There is cross-coupling between the two ends of the mover core of the maglev ruler, resulting in a strongly coupled, nonlinear, multi-input and multi-output system for the levitating system of the maglev ruler. This paper establishes a mathematical model for the levitating system of the maglev ruler, and designs a levitating control system for the maglev ruler based on ADRC control algorithm to achieve decoupling and disturbance suppression. Through simulation analysis and experimental testing of the levitating system with starting and disturbance, it is proved that the levitating control system of the maglev ruler has good dynamic characteristics, static characteristics, and robustness.

This work presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The control system will aim to prevent contaminated water from escaping the network, especially during periods of heavy rainfall, that can cause combined sewer overflows (CSO), while maintaining the flow of water towards the wastewater treatment plant (WWTP) as close as possible to its nominal value. The main purpose of the work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.

This paper explores the application of AI-guided instruction combined with Alternate Reality Games (ARG) to enhance learning performance, specifically focusing on students' learning emotions, engagement, and control beliefs. Based on the Retrieval-Augmented Generation (RAG) Large Language Model (LLM) framework, this study aims to address challenges caused by traditional AI-led instruction, such as emotional detachment and learning disengagement. By integrating immersive ARG experiences, the study evaluates the learning outcomes of students with different cognitive styles in an introductory digital learning course. A qualitative grounded theory approach is used, analyzing data from interviews with 65 students to understand their learning experiences. The findings indicate significant improvement in students' engagement, emotional connections, and sense of control over their learning process when ARG elements were incorporated. Implications for the future of digital learning, as well as recommendations for addressing challenges in AI-enhanced education, are discussed.

Aiming at the problems of high labor intensity and high cost of manual picking, high loss rate of mechanical picking seeds, low mechanization level and difficult to guarantee the quality of picking plates in the process of picking plates of edible sunflower. Based on the principle of manual plate taking, a plate taking control system for sunflower harvesting table was designed. Through the research and analysis of the principle of taking the plate of the edible sunflower harvesting table. According to the actual operation requirements, the overall scheme of the sunflower plate control system is determined, and the control strategy of imitating artificial low loss harvesting is designed. To reduce the grain loss in the process of taking the plate and improve the control accuracy of the system on the movement speed and displacement of the key components in the process of taking the plate, a trapezoidal acceleration and deceleration control algorithm is designed as the control algorithm of taking the plate. To verify the working performance of the plate control system, with the absolute error, relative error and total loss rate of the harvest as objectives. The bench and field verification experiments were both carried out. The bench experiment showed that the speed error of the plate parts was not more than 0.028 m/s. In the bench experiments of the device, the maximum positioning error was 1.25 mm, the average relative error is only 0.94 %, and the grain loss rate was not more than 2.26 %. It result showed that the system algorithm was reliable, the positioning accuracy was high, and the taking plate operation can be completed well. The field verification experiment showed that the forward speed of the unit was in the range of 0.4~0.8 m/s, and the total loss rate of harvest was less than 5 %. When the forward speed is 0.6 m/s, the minimum harvest loss rate is 2.32 %, and which indicated the control system meets the requirements of sunflower harvesting operation.

Electric tractors have many advantages, including high torque, excellent controllability, energy efficiency, simple structure, and electric interface for expansion. However, a significant limitation lies in their endurance. This study presents the design of an extended-range power supply system to ensure continuous endurance of electric tractor. The objective is to provide a continuous power source for our self-developed electric tractor while preserving the benefits of electric propulsion. Extended-range power systems utilize a primary mover, typically an oil-fueled internal combustion engine, to drive the generator for electricity generation, and the generated AC form electricity is subsequently converted into stable DC bus voltage by a power electronic converter. The hardware and control design of the extended-range power supply system have been finalized and validated through experimental trials. Results demonstrate the system’s capability to sustain a stable DC bus voltage amidst disruptions such as sudden load shifts and fluctuations in prime mover speed. Even with 50% sudden load change, the voltage drop is within 12% and could recover to ±3% within 4 seconds. The range extender can be used alone without battery to power the electric tractor, or it can used in parallel with other extended-range extenders or battery thanks with the droop control ability for power sharing.

Starting from the fact that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) type control strategy, is characterised by the fact that the inner flux and torque control loop usually uses hysteresis ON-OFF type controllers and the oscillations introduced by them have to be cancelled out by the outer speed loop controller (which is usually a PI speed controller and whose performance is good around the global operating points and for relatively small variations of the external parameters and disturbances caused in particular by the load torque variation), while retaining the advantages of the DTC strategy, this paper presents the improvement of the performance of the PMSM sensorless control system using Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead-Lag for constant flux, and Sliding Mode Control (SMC) and FOSMC for variable flux. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system provides better performance is verified. The paper also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance of the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM sensorless control system based on the DTC strategy in a complete hardware-in-the-loop (HIL) implementation.

The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factor. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study delves into an approach for estimating the model of quadrotor Unmanned Aerial Vehicles using Physics-Informed Neural Networks (PINNs) when you have a limited amount of data available. PINNs offer the potential to tackle issues like heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability. The effectiveness of the estimator is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF).

The integration of robotic devices into healthcare settings for rehabilitation purposes represents a rapidly advancing field with significant benefits for both therapists and patients. However, standalone robot systems face limitations such as inefficient processing of patient data and logistical challenges for patients, such as travel time to rehabilitation centers. To address these shortcomings, this paper proposes the integration of cyber-physical systems into rehabilitation robotics. Cyber-physical systems leverage network connectivity and real-time data processing to enhance the efficiency and convenience of robot-based rehabilitation. By seamlessly integrating sensors, actuators, and computational algorithms, cyber-physical rehabilitation systems empower therapists to more effectively treat patients by providing actionable insights derived from comprehensive data analysis. This enables therapists to tailor treatment plans precisely to each patient’s unique needs, ultimately leading to improved outcomes. Additionally, cyber-physical rehabilitation systems facilitate remote monitoring and intervention, allowing therapists to oversee patient progress and make timely adjustments to treatment protocols without the need for in-person visits. This not only saves patients valuable time and resources but also ensures continuity of care, particularly for individuals residing in remote or underserved areas. Furthermore, cyber-physical rehabilitation systems enable collaborative decision-making by facilitating communication and information sharing among healthcare professionals, further enhancing the quality and efficacy of rehabilitation services. In summary, integrating cyber-physical systems into rehabilitation robotics represents a significant advancement in leveraging technology to optimize patient care and outcomes, promising to revolutionize rehabilitation therapy.

This paper presents design and optimization of a small-size electromagnetic induction heating control system powered by a 3.7V-900mAh lithium battery and featuring an LC series resonant full bridge inverter circuit, which can be used in small size materials heating applications, such as micro medical devices. The effects of resonant capacitance, inductor wire diameter, heating tube material and wall thickness are studied to maximize the heating rate of the workpiece and meantime reduce the temperature rise of the NMOS transistor. The optimal circuit configuration meeting the design requirements was finally identified via comparing the operational parameters and NMOS transistor loss under different circuit conditions. The validation experiments were conducted on a designed electromagnetic induction smoking devices. The results indicate that under an output current of 4.6A, the heating tube can reach up to the temperature target of 250℃ within 11 seconds, and all NMOS transistors stay below 50°C in a 5 minutes heating process.

: To improve the efficiency of rhizobial inoculation operations and address the current situation of rhizobial inoculation being labor-intensive, a spraying and control system for variable rate application (VRA) of liquid rhizobia was developed. The system can accurately regulate the spraying rate of liquid rhizobia according to the desired rate of rhizobia by incremental Proportional Integral Derivative (PID) closed-loop control algorithm, making it particularly suitable for precise spraying with small amounts. In the laboratory experiment of precise flow control, variation coefficient of spraying nozzle from 0.10 MP to 0.20 MP pressure were no more than 1.2%. A clear linear relationship was observed between the spraying rate and pressure. The maximum response time was 1.93 seconds, while the average response time was 1.62 seconds when controlling the spraying system for small amounts. Field trial was conducted in Heilongjiang Agricultural Xianghe Farm of China. Average seeds number and 100-seed weight of soybean plant with rhizobia inoculation treatment were better than control group, and average yield of spraying liquid rhizobia agent with full base fertilizer increased by 232.5 kg/hm2, representing a significant 7.4% improvement over the control group. The results of these trails clearly showed the positive effect of the spraying and control system for liquid rhizobial inoculation, which has superior to existing inoculation manners, and provides an important technical support for the widespread adoption of rhizobia technology.

Optimal control is a critical tool for mechanical robotic systems, facilitating precise manipulation of dynamic processes. These processes are described through differential equations governed by a control function, addressing a time-optimal problem with bilinear characteristics. Our study utilizes the classical approach complemented by Pontryagin's Maximum Principle (PMP) to explore this inverse optimal problem. The objective is to develop an exact piecewise control function that effectively manages trajectory control while considering the effects of viscous friction. Our simulations demonstrate that the proposed control law markedly diminishes oscillations induced by boundary conditions. This research not only aims to delineate the reachability set but also strives to determine the minimal time required for the process. The findings include an exact analytical solution for the stated control problem.

In the last decades we have experienced great social, cultural, and educational changes, spending more time performing tasks in near vision and less hours in outdoor activities, having more overexposure to digital screens, especially the youngest and increasingly from younger ages. It is common to see cases when performing tasks in near vision the posture or position of the head is not adequate, especially the youngest, being essential to have a correct posture of the head to avoid visual, muscular, or articular problems. We present a procedure to try to control the correct position of the head in real time when performing close-up activities. The aim is to detect the inclination of the head and to know if a bad position is adopted due to a visual problem or simply to inadequate visio-postural habits, warning us of the postural anomaly to correct it.

The goal is the optimal synthesis of the parameters of the control law for a nonlinear satellite attitude control system under constraints related to the saturation effect of reaction wheels. The maximum stability degree of the control system is considered as a criterion for the optimality of the parameters of the control law, and the absolute values of the control torques and angular velocities of the reaction wheels, achievable within the limits of the physical characteristics of the drives, are considered as constraints. The solution to the optimal synthesis problem is based on the transformation of the nonlinear control system model into a linear model, within the framework of the problem of global asymptotic stability of the control system is also guaranteed. The problem is decomposed into a subproblems of obtaining the optimal transient process shape in the control system and considering the constraints. A method of synthesizing the optimal transient process shape based on the criterion of maximum stability degree had been developed. An analytical method of determining the optimal values of the control law parameters, based on decomposing the normalized characteristic polynomial in the linear model into three identical factors had been proposed. A method with regards to the constraints of the control torque and angular speed of the reaction wheels, based on transitioning from relative to real time is developed. An algorithm for calculation of the transition scale from relative to real time, ensuring the fulfillment of all constraints in the optimal synthesis problem, is proposed. An example demonstrating the efficiency and simplicity of the proposed methods and algorithm for targeted selection of technical characteristics of the control system, considering the maximum absolute values of angular speeds and control torques of the reaction wheels, is provided.

Predatory mites are a type of biological control agent used in many countries against various vegetable pests, particularly spider mites. Despite the significant presence of predatory mites in the Tibetan plateau, there is limited research on their fitness and prey capacity in the area. Herein, four predatory mite species, including one species Neoseiulus californicus and three species from genus Amblyseius (Amblyseius swirskii, Amblyseius barkeri, and Amblyseius cucumeris) were investigated. Specifically, their fitness and prey capacity on two-spotted mites (Tetranychus urticae) at highland conditions were evaluated. Compared to the other three predatory species, A. swirskii had the highest fecundity (Mean ± SEM=11.60 ± 0.34) and the highest pre-adult survival rate (Mean ± SEM = 83.33 ± 3.33%). Since their juvenile Survival Rate (SR) was extremely low (Mean ± SEM =13.33% ± 5.77%), most A. barkeri nymphs died before emergence. Compared to the other three predatory mites, A. swirskii had the highest predation capacity on T. urticae adult mites at 15 d post-release (Mean ± SEM =14.28 ± 2.24). Collectively, these findings imply notable advantages in employing A. swirskii for controlling two-spotted mites in the Tibetan Plateau. Furthermore, based on the results, A. swirskii was the most effective, and A. barkeri was the least effective in controlling two-spotted mites in the Tibetan Plateau. These findings could inform the development of a feasible biological control method based on suitable predatory mite species to manage T. urticae in the Tibetan Plateau, and would be helpful in delaying the rise or emergence of chemical pesticide resistance in two-spotted mites.

In this article authors shows chosen problems of technical state diagnosis with the use of identification and technical diagnostics methods such as experimental modal analysis. Relations between methods of dynamic state evaluation and methods of technical state evaluation were indicated. Example modal analysis results illustrate the complexity of projecting dynamic state researches into diagnostic researches of state evaluation.

Effective waste elimination is critical for the success of construction projects. Although several studies have focused on various aspects of construction waste, limited efforts have investigated the dynamic effect of Making-Do (MD) practices on productivity, rework, defects, and materials wastage. From a lean construction perspective, this study aims to address MD waste using the Last Planner System (LPS) and BIM. First, the casual structure that can cause MD in construction projects was expressed in a casual loop diagram (CLD), and thematic analysis uncovered the strategies of LPS-BIM to eliminate MD identified by reviewing the literature. Secondly, twenty-five strategies from the LPS and BIM strategies to address MD using Structural Equation Modeling (SEM) were assessed. Subsequently, a system dynamics model (SDM) for investigating LPS-BIM strategies on MD decisions in a construction project was formulated based on the underlying causal loop diagrams and the mathematical relations among the variables. Finally, the model was applied to three projects, and simulations for four LPS-BIM scenarios were carried out. The findings show that dynamic interactions among diverse production planning and control factors are critical in evaluating MD impacts on a construction project. The results demonstrate that the LPS-BIM approach resulted in an average 43.8% reduction in the tasks performed with MD, 45.3% of constraints, 66.5% of construction waste, increasing 13.7% completion rate, and 29.3% cost reduction, demonstrating that LPS-BIM as a more efficient solution for MD mitigation and construction planning.

Chaotic systems are systems whose results are sensitive to the initial conditions. Chaotic systems generally have nonlinearities that can be difficult to model. One example of a chaotic system with nonlinearities is the Lorenz system. The Lorenz system can be used to model weather, wind disturbances, and electronic circuit design, among other applications. In this manuscript, the Lorenz system is modeled and various control methods are applied in an effort to dictate the system’s state and rate trajectories. The combination of the linear feedback and nonlinear feedforward controllers can show over an 80% improvement in state trajectory errors when compared to the baseline run. However, the improved state trajectory performance comes at a cost of as much as 1800% error in the rate trajectories.

In this paper, an electromechanical mass lifter system is designed, analyzed and compare using optimal and robust control theories. LQR and μ -synthesis controllers are used to improve the lift displacement by comparison method for tracking the desired step and sinusoidal wave signals input. Finally, the comparison simulation result prove the effectiveness of the electromechanical mass lifter system with μ -synthesis controller for improving the rise time, percentage overshoot, settling time and peak value of tracking the desired step displacement signal and improving the peak value for tracking the desired sinusoidal displacement signal with a good performance.

This paper presents a novel approach to power management techniques in energy storage system (ESS) interoperability with the energy power system (EPS) by inserting a SCADA controlled directional protection relay for improving the mode switching sequence. According with latest European standards, all necessary measures for re-synchronization and reconnection for health work of the system must be taken. The proposed approach intends to improve the “interface” between storage systems and electric power grid by using a directional relay which provides interoperability improvement by automatic recognizing and differentiating the switching sequence of the mode of operation from the fault mode. Simulated results from a small scale electrical generating system are provided. The results demonstrate that the proposed power management solution is technically ideal to achieve high quality energy while retaining the stability of the power grid network. The interoperability of PHS systems integrated with the electric power grid usually leads to perturbations in terms of frequency of the generated voltage, and for improving it we designed an automatic treating and re-connecting software module. This work regards the importance of producing electricity from green and renewable energy sources, while retaining the stability and reliability of the power grid. ESS systems are widely used to help balancing of intermittent forms of load demand on the electrical power grid at specific key moments. In this article, the pumped hydro storage (PHS) technologies are proposed as an economical way to utilize smoothly the off-peak energy or surplus energy, by pumping water to a reservoir at a higher level. During peak load periods, the stored water is discharged through the reversible pump-turbine to generate electricity to meet the peak demand controlled by designed SCADA application.

In this paper, modelling designing and simulation of a simple voltage amplidyne system is done using robust control theory. In order to increase the performance of the voltage amplidyne system with H ∞ optimal control synthesis and H ∞ optimal control synthesis via ∞-iteration controllers are used. The open loop response of the voltage amplidyne system shows that the system can amplify the input 7 times. Comparison of the voltage amplidyne system with H ∞ optimal control synthesis and H ∞ optimal control synthesis via ∞-iteration controllers to track a desired step input have been done. Finally, the comparative simulation results prove the effectiveness of the proposed voltage amplidyne system with H ∞ optimal control synthesis controller in improving the percentage overshoot and the settling time.

In this paper, we investigated the formation isolation regulation issue regarding multiple autonomous underwater vehicles (AUVs) characterized by a "leader-follower" framework. Considering the cooperative-competitive relationship among the follower AUVs and the impact of unknown external disturbances, an extended state observer was designed based on backstepping to mitigate these disturbances, and an event-triggered control scheme was designed to realize the two-part consensus control within the multi-AUV system. Through rigorous theoretical analysis, it is shown that the system achieves asymptotic steadiness and is free from zeno behavior under the proposed event-triggered control scheme. Finally, numerical simulations confirm the efficiency of the regulation strategy in achieving formation separation within the multi-AUV, where the trajectory tracking errors of individual AUVs gather in a compact vicinity close to the source and the structure converges was achieved, with the absence of zeno behavior also demonstrated.

This paper presents three types of sliding mode controllers for a magnetic levitation system. First, a proportional-integral sliding mode controller (PI-SMC) is designed using a new switching surface and a proportional plus power rate reaching law. The PI-SMC is more robust than a feedback linearization controller in the presence of mismatched uncertainties and outperforms the SMC schemes reported recently in the literature in terms of the convergence rate and settling time. Next, to reduce the chattering phenomenon in the PI-SMC, a state feedback-based discrete-time SMC algorithm is developed. However, the disturbance rejection ability is compromised to some extent. Furthermore, to improve the robustness without compromising the chattering reduction benefits of the discrete-time SMC, mismatched uncertainties like sensor noise and track input disturbance are incorporated in a robust discrete-time SMC design using multirate output feedback (MROF). With this technique, it is possible to realize the effect of a full-state feedback controller without incurring the complexity of a dynamic controller or an additional discrete-time observer. Also, the MROF-based discrete-time SMC strategy can stabilize the magnetic levitation system with excellent dynamic and steady-state performance with superior robustness in the presence of mismatched uncertainties. The stability of the closed-loop system under the proposed controllers is proved by using the Lyapunov stability theory. The simulation results and analytical comparisons demonstrate the effectiveness and robustness of the proposed control schemes.

This paper presents a new means to control the processes, which involve the electric energy conversion. Electric machines fed by electronic converters provide the useful power defined by the inner product of two generalized energetic variables: the effort and the speed. The novelty of the paper idea is to control the desired energetics variables by a direct Data Driven Control (DDC) law in terms of the pair effort, speed and corresponding voltage controls of the electronic converter. It must be mentioned that the same desired useful power might be obtained with different controls at different efficiencies. The regularization problem was solved by selecting from a knowledge database, the maximum efficiency operation points. Knowing a reasonable number of optimal efficiency operation points, an interpolative Radial Base Function (RBF) control was built up. The RBF algorithm can find by interpolation the optimal controls for any admissible operation points of the process. The control scheme developed for this purpose has an inner DDC loop, that perform the converter control based on measured speed and demanded torque by the outer loop which handles the speed. A comparison of the DDC with the Model Predictive Control (MPC) schemas highlights the advantages of the new proposed control.

In the present study, we introduce the two dimensional Chlodovsky type Bernstein operators based on (p,q)−integer. We examine approximation properties of our new operator by the help of Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence by means of the modulus of continuity and the Lipschitz type maximal function. Also, we give a Voronovskaja type theorem for this operators. And, we investigate weighted approximation properties of these operators and estimate rate of convergence in the same space. Finally, with the help of Maple, illustrative graphics show the rate of convergence of these operators to certain functions . The optimization of approximation speeds by operators during system control provides significant improvements in stability and performance. As a result, the control and modeling of dynamic systems become more efficient and effective through innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications in these areas.

With the development of artificial intelligence technology, manipulators, as an important part of the work of intelligent robots, can replace humans to perform various complex tasks. In recent years, there have been many studies on the remote control of manipulators using various control technologies. Starting from the manufacturing materials of flexible sensors used for manipulators, this paper introduces the base materials, sensing materials, and flexible electrode materials of flexible sensors, respectively, and summarizes the performance of flexible sensors made based on the characteristics of different materials. The basic principles of dipping and coating, lithography, inkjet printing, screen printing, 3D printing and so on are introduced from the perspective of flexible sensor manufacturing process. Some different functions of sensors achieved by different structural designs, such as: tensile cracking, microchannels, etc. are also introduced. Then, from the perspective of recognition accuracy of the manipulator, two kinds of control methods based on flexible sensor by data glove control and surface EMG control are classified and introduced, and the problems existing in three aspects of flexible sensor material preparation, manufacturing process and control series design are summarized. Finally, the possible future research directions in this field are suggested.

Recent research on fractional order control laws has introduced the fractional calculus concept into the field of control engineering. As described herein, we apply fractional order LQR control to a current-controlled attractive-force type magnetic levitation system, which is a strongly nonlinear and unstable system, to investigate its control performance through experimentation. First, to design the controller, a current-controlled attractive-force type magnetic levitation system expressed as an integer-order system is extended to a fractional order system expressed using fractional order derivatives. Then target value tracking control of levitated objects is achieved by adding states, described by the integrals of the deviation between the output and the target value, to the extended system. Next, a fractional order LQR controller is designed for the extended system. For state-feedback control such as fractional order servo LQR control, which requires information of all states, a fractional order state-observer is configured to estimate fractional order states. Simulation results demonstrate that fractional order servo LQR control can achieve equilibrium point stabilization and enable target-value tracking. Finally, to verify the fractional order servo LQR control effectiveness, experiments using the designed fractional order servo LQR control law are done with comparison to conventional integer-order servo LQR control.

Abstract. This paper proposes a Second Order Terminal Sliding Mode (2TSM) approach to the trajectory tracking of Differential Drive Mobile Robot (DDMR). Within this cascaded control scheme, the 2TSM dynamic controller, at the inner most loop, tracks the robot’s velocity quantities; while a kinematic controller, at the outer most loop, regulates the robot’s positions. In this manner, chattering is greatly attenuated and finite time convergence is guaranteed by the second order TSM manifold which involves higher order derivatives of the state variables, resulting in inherently robust as well as fast and better tracking precision. The simulation results demonstrate the merit of the proposed control methods.

In large-capacity energy storage systems, instructions are decomposed typically using an equalized power distribution strategy, where clusters/modules operate at the same power and durations. When dispatching shifts from stable single conditions to intricate coupled conditions, this distribution strategy inevitably results in increased inconsistency and hastened system aging. This paper presents a novel differentiated power distribution strategy comprising three control variables: the rotation status, the operating boundaries for both Depth of Discharge (DOD) and C-rates (C) within a control period. The proposed strategy integrates an aging cost prediction model developed to express the mapping relationship between these control variables and aging costs. Additionally, it incorporates the multi-colony particle swarm optimization (Mc-PSO) algorithm into the optimization model to minimize aging costs. The aging cost prediction model consists of three functions: predicting health features (HFs) based on the cumulative charge/discharge throughput quantity and operating boundaries, characterizing HFs as comprehensive scores, and calculating aging costs using both comprehensive scores and residual equipment value. Further, we elaborated on the engineering application process for the proposed control strategy. In the simulation scenarios, this strategy prolonged the service life by 14.62%, reduced the overall aging cost by 6.61%, and improved module consistency by 21.98%, compared with the traditional equalized distribution strategy. In summary, the proposed strategy proves effective in elongating service life, reducing overall aging costs, and increasing the benefit of energy storage systems in particular application scenarios.

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems, and lays the foundation for guiding component repair and maintenance.

At present, aeroponic control is mainly regulated according to the environmental factors of aeroponics, such as the temperature, humidity, light intensity, carbon dioxide concentration, pH, and electrical conductivity (EC) of the nutrient solution. There is a lack of knowledge available in the literature for an aeroponic control system for plant growth information as the primary feedback signal. Therefore, a fuzzy logic-controlled aeroponics system was developed for the growth of lettuce as a feedback signal. After designing, the effectiveness test of system was performed with experiment having three treatments T1 (180C), T2 (220C) and without control (CK). The results show an increasing trend in shoot length, root length, and shoot area over time in all treatments. In terms of shoot area, the maximum of 345 cm² was observed in the T1 treatment, while the CK treatment had a minimum of 241.8 cm². Statistical analysis indicated that the T1 and T2 treatments were not significantly different, but both were significantly different from the CK treatment. The study demonstrates the effectiveness of the fuzzy logic control system and an 18°C nutrient solution temperature, resulting in improved lettuce growth and reduced root-to-shoot ratio. The findings suggest the potential of using fuzzy logic control in aeroponics for optimized cultivation outcomes.

This paper is primarily focused on the robust control of an inverted pendulum system based on the policy iteration in reinforcement learning. First, a mathematical model of the single inverted pendulum system is established through a force analysis of the pendulum and trolley. Second, based on the theory of robust optimal control, the robust control of the uncertain linear inverted pendulum system is transformed into an optimal control problem with an appropriate performance index. Moreover, for the uncertain linear and nonlinear systems, two reinforcement-learning control algorithms are proposed using the policy iteration method. Finally, two numerical examples are provided to validate the reinforcement learning algorithms for the robust control of the inverted pendulum systems.

Nowadays brushless DC motors (BLDCMs) are becoming indispensable components as the electrification revolution in the mobility industry is happening. Electric kick scooters, so-called e-scooters, are among these micro-mobility vehicles which are powered by these motors. Due to the uncertain and nonlinear features, the controller performance developed for these motors degrades. For these reasons, a chattering-reduced cascaded Sliding Mode Control (SMC) scheme to effectively track reference motor speed in the outer loop by eliminating torque ripples in the inner loop current control was designed. Field-oriented Control (FOC) methodology was used to implement the SMC in the BLDCM. An exponential reaching law algorithm was proposed for sliding surfaces of the inner and outer loop controllers. The suitability and performance of electric scooter-hub motors were analyzed in terms of traction control. A cascaded speed and torque controller produced significantly favorable results representing minimized torque and current ripples, and operation over a wide speed range.

This paper proposes a novel hybrid control strategy for quadrotor unmanned aerial vehicles (UAVs) based on bio-inspired neural dynamics. This strategy addresses the velocity jump issue prevalent in traditional backstepping control and the control signal chattering problem common in traditional sliding mode control. The control system comprises an outer-loop bio-inspired backstepping controller and an inner-loop bio-inspired sliding mode controller, ensuring smooth trajectory tracking even in the presence of external disturbances. The stability of the quadrotor UAV control system is rigorously analyzed using Lyapunov stability theory. Extensive comparative simulation experiments are conducted to assess the robustness and effectiveness of the control system. Results demonstrate superior trajectory tracking performance of the proposed control system compared to other control systems for quadrotor UAVs.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

In the present study, we introduce the two dimensional Chlodovsky type Bernstein operators based on (p, q)−integer. We examine approximation properties of our new operator by the help of Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence by means of the modulus of continuity and the Lipschitz type maximal function. Also, we give a Voronovskaja type theorem for this operators. And, we investigate weighted approximation properties of these operators and estimate rate of convergence in the same space. Finally, with the help of Maple, illustrative graphics show the rate of convergence of these operators to certain functions . The optimization of approximation speeds by operators during system control provides significant improvements in stability and performance. As a result, the control and modeling of dynamic systems become more efficient and effective through innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications in these areas.

In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used to design various control strategies such as: proportional-integral-derivative (PID), linear-quadratic regulator (LQR), pole placement-based state feedback, observer-based output feedback, loop transfer recovery (LTR)-based linear-quadratic-Gaussian (LQG), and nonlinear feedback-linearizing control for the SMIB system. The controllers developed are then validated on the high-fidelity physics-based truth model of the SMIB system. Finally, a comparison is made of the performance of the controllers at different operating points of the SMIB system.

When wood is thermally modified, several chemical reactions take place that change the chemical and physical properties of the wood. These changes correlate with the degree of modification, which is mostly a function of temperature and duration of modification, and consequently with a mass loss during this process. There is a lack of standardized quality control to verify the degree of heat treatment of wood and thus its quality. One of the possible methods to check the degree of thermal modification of a particular type of wood is thermogravimetry (TG). It is based on the assumption that processes that did not take place during thermal modification continue when the TG experiment is carried out. In this method, calibration curves have to be established based on TG measurements of standard samples that have been thermally modified at different temperatures and whose mass loss during modification is known. The calibration curves show the mass loss during the TG measurement as a function of the mass loss during the previous thermal modification. The course of thermal decomposition during the TG measurements is influenced by many parameters, such as the mass of the sample, the heating rate, the atmosphere in which the measurement takes place, and the shape of the crucible in which the sample is placed. In this paper, the influence of these parameters on the calibration curves was investigated. We have focused on oak wood. The best parameters result in a calibration curve with the largest correlation coefficient R2 and the highest slope of the line k. On this basis, we can determine the mass loss during thermal modification of unknown samples of the same wood species under the same measurement conditions.

Today, transit control systems go beyond simple controllers located at the intersections of our streets, involving large companies in the field, which with the implementation and use of sophisticated equipment encompass endless new and advanced technologies that manage to give control to the massive automotive park, thus ensuring fluidity and road safety. Many of these systems are used in the big world capitals, which is why the model used in Santiago, Chile is a system applied and brought directly by the SIEMENS Company of England (specifically the system used in the City of London). It is capable of transmitting the different control signals in a similar and digital way from the different interconnected devices in and out of the road infrastructure.

As the penetration of renewable energies increases, their acceptance of the power grid system is becoming an important issue. In the case of South Korea, a 22.9kV transformer is frequently utilized to reduce the power loss of the generated renewable energy transmission, so the power quality at the point of common connection (PCC) is inevitably influenced by the nonlinearity of the transformer and the uncertainty of the inductance and resistance of the transmission line. In this paper, we present a point of common connection voltage-modulated direct power control (PCCVM-DPC) system that enhances power quality at the PCC. First, we present the mathematical dynamics of active and reactive power at the PCC side, which contains the step-up transformer’s nonlinearity, the grid voltage harmonics, and the parameter uncertainties. By analyzing the disturbance terms of the PCCVM-DPC system, we present the disturbance observer (DOB) for the PCCVM-DPC system. To assess the efficacy of our approach, we perform comparative studies of the PCCVM-DPC without DOB and PCCVM-DPC with DOB by constructing the simulation environment based on the commercial step-up transformer and ESS inverter datasheet. We have validated that the active and reactive power control performance of the PCCVM-DPC with DOB outperforms the PCCVM-DPC without DOB from the observation that the current total harmonic distortion reduced by more than 40% compared to the PCCVM-DPC without the DOB.

This study proposes and validates a novel combustion control system for Oil-Fired Boilers aimed at reducing air pollutant emissions through flame image prediction. The proposed system is easily applicable to existing ships. Traditional proportional combustion control systems supply fuel and air at fixed ratios according to the set steam load, without considering the emission of air pollutants. To address this, a stable and immediate control system is proposed, which adjusts the air supply to modify the combustion state. The combustion control system utilizes oxygen concentration predictions from flame images via SEF+SVM as control inputs, and applies Internal Model Control (IMC)-based Proportional-Integral (PI) control for real-time combustion control. Due to the complexity of modeling the image-based system, IMC parameter tuning through experimentation is essential for achieving effective control performance. Experiments conducted on a 3000 kg/h marine oil-fired boiler in actual operation optimized the controller parameters for stability and responsiveness, and validated their effectiveness. The results demonstrate the potential of the proposed system to improve combustion efficiency and reduce emissions of air pollutants. This study provides a feasible and effective solution for enhancing the environmental performance of marine oil-fired boilers. Given its ease of application to existing ships, it is expected to contribute to sustainable air pollution reduction across the maritime environment.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.

The growing use of proton-exchange membrane fuel cells (PEMFC) in hybrid propulsion systems, aimed at replacing traditional internal combustion engines and reducing greenhouse gas emis-sions. Effective power distribution between the fuel cell and the energy storage system (ESS) is crucial, which has led to a growing emphasis on developing energy management systems (EMS) to efficiently implement this integration. To address this goal, this study examines the performance of a fuzzy logic rule-based strategy for a hybrid fuel cell propulsion system in a small hydro-gen-powered passenger vessel. The primary objective is to optimize fuel efficiency, with particular attention to reducing hydrogen consumption. The analysis is carried out under typical operating conditions encountered during a river trip. Comparisons between the proposed strategy with other approaches —control-based, optimization-based and deterministic rule-based— are con-ducted to verify the effectiveness of the proposed strategy. Simulation results indicated that EMS based on fuzzy logic mechanisms was the most successful in reducing fuel consumption. The superior performance of this method stems from its ability to adaptively manage power distri-bution between the fuel cell and energy storage systems.

The Food and Drug Administration’s 2011 Process Validation Guidance and International Council for Harmonization Quality Guidelines recommend continued process verification (CPV) as a mandatory requirement for pharmaceutical, biopharmaceutical, and other regulated industries. As a part of product life cycle management, after process characterization in stage 1 and process qualification and validation in stage-2, CPV is performed as stage-3 validation during commercial manufacturing. CPV ensures that the process continues to remain within a validated state. CPV requires the collection and analysis of data related to critical quality attributes, critical material attributes, and critical process parameters on a minimum basis. Data is then used to elucidate process control regarding the capability to meet predefined specifications and stability via statistical process control tools. In statistical process control, control charts and Nelson rules are commonly used throughout the industry to monitor and trend data to ensure that a process remains in control. In the control chart, the mean value is taken as the center line, and 3σ limits are placed on either side of the center line. In CPV, manufactured lots are summarized, trended, and discussed at a cross-functional level at pre-defined intervals (either weekly, bi-weekly, or even monthly) based on the batch run rate. A cross-functional team assesses details regarding 1) violation frequency, 2) statistical strength of the signal, 3) violation magnitude and its impact on process or quality, and 4) any potential root causes for out-of-expectation or out-of-trend signals. The cross-functional team must analyze whether any signal identified by the control chart is reliable and must determine whether corrective action should be taken and justify the decision in the CPV meeting minutes based on statistical strength and reliability of the signal. However, basic control charts are susceptible to false alarms and nuisance alarms. Therefore, it is imperative to understand the assumptions behind control charts and the inherent false alarm rates for different Nelson rules. In this article, the authors have detailed the assumptions behind the usage of control charts, the rate of false alarms for different Nelson rules, the impact of skewness and kurtosis of a data distribution on the false alarm rate, and methods for optimizing control chart design by reducing false alarm rates and nuisance signals.

Keywords: Rehabilitation robot, position-force control, feed-forward control.

Dynamic systems which underly controlled systems are expected to increase in complexity as robots, devices, and connected networks become more intelligent. While classical stable systems converge to a stable point (a sink), another type of stability is to consider a stable path rather than a single point. Such stable paths can be made of saddles which draw in trajectories from certain regions, and then push the trajectory toward the next saddle point. These are chains of saddles are called stable heteroclinic channels (SHCs), and can be used in robotic control to represent time sequences. While we have previously shown that each saddle is visualizable as a trajectory waypoint in phase space, how to increase the fidelity of the trajectory was unclear. In this paper, we hypothesized that the waypoints can be individually modified to locally vary fidelity. Specifically, we expected that increasing the saddle value (ratio of saddle eigenvalues) causes the trajectory to slow to more closely approach a particular saddle. Combined with other parameters that control speed and magnitude, a system expressed with an SHC can be modified locally, point by point, without disrupting the rest of the path, supporting their use in motion primitives. However, even more complex trajectory shape modifications are possible. While some combinations can enable a trajectory to better reach into corners, other combinations can rotate, distort and round the trajectory in the region of a corner. In our example, modifying select saddle values produced a 32% decrease in the trajectory error of a complex trajectory produced using this system. This is an effect not visible in previous 1D studies, that can lead to different learnable and tunable representations of dynamic systems.

This study uses simulation and experimental methodologies to explore the efficacy of bi-sectional horizontal kinetic shading systems (KSS) featuring horizontal fins in enhancing daylight comfort across various climates. Given the increasing energy demands and shifting climatic patterns, optimizing daylight levels while minimizing solar heat gain is vital. The paper introduces a bespoke horizontal bi-sectional KSS, simulated in three distinct climates—Wroclaw, Tehran, and Bangkok—using Climate Based Daylight Modelling methods with Ladybug and Honeybee tools in Rhino software. The study employs standard daylight metrics such as Useful Daylight Illuminance (UDI) and Daylight Glare Probability (DGP), along with custom metrics designed to capture the specific dynamics of the bi-sectional KSS. Initial simulations indicated that the KSS significantly improved daylight distribution and uniformity, reducing glare and over-illumination risks by av. 42.34%. Including KSS switching schedules, which are seldom detailed in similar research, enhances the reproducibility and understanding of findings. Subsequently, a physical reduced-scale mock-up of the bi-sectional KSS was tested under real-weather conditions in Wroclaw (lat. 51° N) during June-July 2024. The mock-up utilized two chambers: one equipped with the prototype of bi-sectional KSS and the second without any protection. The operation of fins was guided by two stepper motors controlled by a Python script running on a Raspberry Pi 3 minicomputer. The control chamber served to benchmark the bi-sectional KSS's efficiency by providing results that would be expected if no shading system were installed. The experimental outcomes supported the simulations, demonstrating the KSS's robustness in reducing high illuminance levels to the target comfort level, thus enhancing indoor visual comfort. The values below 3,000 lux were maintained for 68% of the time. However, it must be explicitly stated that during the June-July 2024 heat waves, the illuminance levels inside the test room momentarily exceeded the comfort threshold of 3,000 lux, reaching up to 4,674 lux. Through quantitative and qualitative analyses, the paper advocates for the broader application and further development of KSS as a climate-responsive shading system in diverse architectural contexts.

Traditional aquaculture systems appear to be confronted with the high total ammonia nitrogen (TAN) produced during production, which can be harmful to aquatic life. As the demand for global fish production continues to increase, farmers should adopt Recirculating Aquaculture Systems (RAS) to improve production. The biofilter plays a vital role in ammonia removal. However, the drawback of the biofilter operation is needed an automatic and controlled system with a water quality monitoring system to guarantee optimal performance. RAS utilize a monitoring and control system to maintain the water quality and control the biofilm growth in the biofilter to reduce the ammonia. Therefore, this study focuses on developing a water quality monitoring and control system to achieve optimum biofilm performance. From 35 days of the experiment, the water quality was controlled at a level save for the aquatic environment, such as dissolved oxygen, pH, temperature, and TDS. Thus, the highest TAN removal efficiency is 50% when biofilm thickness reaches 119.88 µm.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

The numerical implementation of the controllers allows the use of very complex algorithms with ease. However, in practice, due to its proven advantages, the PID controller (and its variants) is widely used in industrial control systems as well as in many other applications that require continuous control. Most of the methods for tuning the parameters of PID controllers are based on time-invariant linear models of the processes, which in practice can lead to poor performance of the control system. The paper presents an application of reinforcement learning algorithms in tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of PID controllers is done with the help of Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm which presents a series of advantages compared to other similar methods from Machine Learning dedicated to continuous state and action spaces. TD3 algorithm is an off-policy Actor-Critic based method and was used as it does not require a system model. The presented technique is applied for control of a biotechnological system which has a strongly nonlinear dynamic. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through simulation illustrating the effectiveness of the proposed methodology.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing the sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with the uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems with a focus on the cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in the offline and online process optimization approaches have been presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, and reduced energy consumption and carbon emissions are independently investigated for the offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, the paper explores the challenges and opportunities inherent in developing a robust cyber-physical optimization system

This study presents a vibration control using actively tunable vibration absorbers (ATVA) to suppress vibration of a thin plate. The ATVA’s is made of a sandwich hollow structure embedded with the electrorheological fluid (ERF). ERF is considered to be one of the most important smart fluids and it is suitable to be embedded in a smart structure due to its controllable viscosity property. ERF’s apparent viscosity can be controlled in response to the electric field and the change is reversible in 10 microseconds. Therefore, the physical properties of the ERF-embedded smart structure, such as the stiffness and damping coefficients, can be changed in response to the applied electric field. A mathematical model is difficult to be obtained to describe the exact characteristics of the ERF embedded ATVA because of the nonlinearity of ERF’s viscosity. Therefore, a fuzzy modeling and experimental validations of ERF-based ATVA from stationary random vibrations of thin plates are presented in this study. Because Type-2 fuzzy sets generalize Type-1 fuzzy sets so that more modelling uncertainties can be handled, a semi-active vibration controller is proposed based on Type-2 fuzzy sets. To investigate the different performances by using different types of fuzzy controllers, the experimental measurements employing type-1 fuzzy and interval type-2 fuzzy controllers are implemented by the Compact RIO embedded system. The fuzzy modeling framework and solution methods presented in this work can be used for design, performance analysis, and optimization of ATVA from stationary random vibration of thin plates.

This paper develops a methodology to control the navigation of an autonomous ship robot from an initial state to a final. To solve the problem the following approach is used: The ship robot system is modelled as a control system of six ordinary differential equations involving six state variables and three control variables. After having computed the system Hamiltonian, the feasible controls for optimality are derived from the normal equations of optimality as functions of the state and costate variables. Such controls are substituted into the state and the costate equations and give a combined control-free state-costate system of ordinary differential equations which is solved numerically by using a developed Matlab computer program. Associated computational simulations are designed and provided to show the reliability of the approach.

Fully mechanized mining face production involves complex geological conditions; mining pressure; gas, water, fire and other mining environments. The working face needs a shearer, hydraulic support, a scraper conveyor and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for coal mining robot technology systems, including single robots, robotized equipment, robot clusters and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve customized research and development and standardized production of various types of robots. A specific analysis was conducted on the research progress of common key technologies such as explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mine robot research and development is proposed. Moreover, robotic mining systems should be promoted to enhance the overall intelligence level and mining efficiency of mining equipment. To develop human‒machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots. The digital twinning of the entire mine robot system should be accelerated, the normalized operation level of coal mine robots should be improved, research on coal mining robots, support robots, and transportation robots should be carried out, intelligence should be achieved in fully mechanized mining faces, and equipment support for fewer and unmanned fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization has been summarized, and the technical and engineering feasibility of the coal mining machine population has been verified.

The suspended test masses of gravitational-wave (GW) detectors require precise alignment to be able to operate the detector stably and with high sensitivity. This includes the continuous counter-acting of seismic disturbances, which, below a few Hertz, are not sufficiently reduced by the seismic isolation system. The residual angular motion of suspended test masses is further suppressed by the Angular Sensing and Control (ASC) system. However, in doing so, the angular motion can be enhanced by the ASC at higher frequencies where the seismic isolation system is very effective. This has led to sensitivity limitations between about 10 Hz and 25 Hz of the LIGO detectors in past observation runs. The observed ASC noise was larger than simple models predict, which means that more accurate detector models and new simulation tools are required. We present Lightsaber, a new time-domain simulator of the ASC in LIGO. The simulation is a nonlinear simulation of the optomechanical system consisting of the high-power cavity laser beam and the last two stages of suspension in LIGO including the ASC. The main noise inputs are power fluctuations of the laser beam at the input of the arm cavities, read-out noise of sensors used for the ASC, displacement noise from the suspension platforms, and noise introduced by the suspension damping loops. While the plant simulation uses local degrees of freedom of individual suspension systems, the control is applied on a global angular basis, which requires a conversion between the local and global bases for sensing and actuation. Some of the studies that can be done with this simulation concern mis-centering of the beam-spot (BS) position on the test masses, the role of laser power fluctuations for angular dynamics, and the role of the various nonlinear dynamics.

Most systems that are the subject of control engineering studies have some non-linearity. An example of this is the horizontal cylindrical tank, commonly used in process industries. To deal with cases like this, several control theories have been developed over time, each one presenting better results in certain systems. This work presents an alternative for the control of nonlinear systems, without necessary modeling or previous information about the system, based on a new optimization law for the artificial neural network training in real time.

Precise and fast position tracking is essential for the correct operation of many industrial robots and CNC machine tools. This subject is also important in the control of the mount of the astronomical telescope, especially for the tracking of artificial satellites. As system parameters can change, a control method that is robust to changes in parameters must be used. Such a method is the sliding control, which, however, ensures the robustness only after reaching the sliding surface. Therefore, a new method was proposed in the paper, which eliminates the phase of reaching the sliding surface. The method consists of using a reference trajectory generator and determining the generalized error in relation to this trajectory. The procedure for designing the control system is presented. Next, the proposed method was verified on the laboratory stand. The described control method provides a robust system operation and can be easily implemented in the control system.

The quality of cellular products used in biological research can directly impact the ability to ob-tain accurate results. Epstein-Barr virus (EBV) is a latent virus that spreads extensively world-wide, and cell lines used in experiments may carry EBV and pose an infection risk. The presence of EBV in a single cell line can contaminate other cell lines used in the same laboratory, affecting experimental results. We developed three EBV detection systems: (1) a polymerase chain reac-tion (PCR)-based detection system, (2) a recombinase polymerase amplification (RPA)-based de-tection system, and (3) a combined RPA-lateral flow assay (LFA) detection system. The mini-mum EBV detection limits were 1×103 copy numbers for the RPA-based and RPA-LFA systems and 1×104 copy numbers for the PCR-based system. Both the PCR and RPA detection systems were applied to 192 cell lines, and the results were consistent with those obtained by the EBV assay methods specified in the pharma-ceutical industry standards of the People's Republic of China. 10 EBV-positive cell lines were identified. The combined RPA-LFA system is simple to operate, allowing for rapid results visualization. This system can be implemented in laborato-ries and cell banks as part of a daily quality control strategy to ensure cell quality and experi-mental safety and may represent a potential new technique for the rapid detection of EBV in clinical samples.

The ever-growing concerns about global warming and the rapid depletion of fossil fuels have triggered a rising interest in research for cleaner, more efficient and cost-effective energy generation. Organic Rankine cycle systems have immense potential to become a strong alternative to conventional methods of energy generation. However, such systems’ efficiency is limited by the performance of the incorporated gas expanders or prime movers. Conventional gas expanders often utilize ports or cam-operated valves. Ported expanders offer limited efficiency and controllability, and produce high emissions causing a large amount of heated and compressed gas to be wasted. Cam-operated valves, in contrast, increase expansion performance compared to ported expanders, however, they have no variability and adaptability to changes in system parameters such as gas pressure, temperature, dryness fraction, and load variation to name a few. On a positive note, this issue can be addressed by adopting a variable timing and fast-operating, control valve with an accurate and adaptive control mechanism at the expander inlet. A properly designed and controlled valve can greatly improve the system performance of gas expanders and pave the way for an efficient low-cost alternative energy generation. This manuscript provides a comparative review of recent progress on the design, modeling, optimization, and control aspects of valves for gas expanders. A clear pathway on the scope of further development is also drawn based on the present state of the art.

In contemporary construction, the prevalence of vibration serviceability issues in lightweight and slender structures has become increasingly common, owing to advancements in building materials and construction methods. While these structures often meet the criteria for ultimate limit states, they can still elicit complaints due to excessive vibrations induced by human activity. To address this challenge, the Integral Resonant Control (IRC) technique has emerged as a favored approach for actively damping vibrations in various systems. This study introduces a fresh perspective on implementing a multi-input multi-output (MIMO) IRC scheme for active vibration control (AVC) specifically tailored for pedestrian structures utilizing inertial mass actuators. Building upon a common framework and design methodology outlined in previous research, this work presents a novel application of MIMO IRC for AVC. The designed controller is rigorously tested and implemented on a laboratory floor structure to validate its efficacy.

The idea of developing a robot controlled by iris movement to assist physically disabled individuals is indeed innovative and has the potential to significantly improve their quality of life. This technology can empower individuals with limited mobility and enhance their ability to interact with their environment. Disability of movement has huge impact on the life of physically disabled people. Therefore, there is need to develop a robot which can be controlled using iris movement. The main idea of this work revolves around iris recognition from an eye image, specifically identifying the centroid of the iris. The centroid's position is then utilized to issue commands to control the robot. This innovative approach leverages iris movement as a means of communication and control, offering a potential breakthrough in assisting individuals with physical disabilities. The proposed method aims to improve the precision and effectiveness of iris recognition by incorporating advanced segmentation techniques and fuzzy clustering methods. The fast gradient filters using a fuzzy inference system (FIS) is employed to separate the iris from its surroundings. Then, the bald eagle search (BES) algorithm is employed to locate and isolate the iris region. Subsequently, the Fuzzy KNN algorithm is applied for the matching process. This combined methodology aims to improve the overall performance of iris recognition systems by leveraging advanced segmentation, search, and classification techniques. The results of the proposed model are validated using True Success Rate (TSR) and compared to other existing models. The results highlight the effectiveness of the proposed method for the 400 tested images representing 40 people.

ACL injuries present significant challenges in sports medicine, particularly in restoring knee joint function and preventing further complications like subsequent injuries or post-traumatic osteoarthritis. Traditional rehabilitation techniques, which often rely on pre-planned exercises and standardized assessments, may not adequately prepare athletes for the unpredictable nature of sports. This paper argues for a comprehensive approach that recognizes ACL injuries as failures of complex nonlinear systems rather than merely biomechanical deficiencies. The complexity of ACL injuries involves a dynamic interplay of individual skills, team dynamics, and external conditions. Traditional rehabilitation approaches that focus solely on biomechanical factors overlook the critical role of interpersonal dynamics and sensorimotor control. The brain’s integration of sensory information to form a comprehensive environmental representation is vital for motor actions. Disruptions in this sensorimotor feedback loop can impair motor control, increasing the risk of ACL injuries. Effective rehabilitation should enhance sensory integration and feedback mechanisms, improving motor control and reducing re-injury risk. Addressing proprioceptive deficits and arthrogenic muscle inhibition (AMI) is crucial for successful rehabilitation. Neurocognitive training, focusing on enhancing decision-making and motor responses, is essential for sports performance and injury prevention. Techniques like neurofeedback training can optimize brain activity patterns, leading to better motor responses and adaptation to dynamic environments. Psychological factors such as motivation, anxiety, and fear significantly influence recovery. Integrating psychological support into rehabilitation programs can address these factors, leading to more comprehensive and effective recovery strategies. Innovative rehabilitation approaches, including EMG biofeedback, motor imagery, and virtual reality-based training, can enhance reactive motor planning and adaptability, preparing athletes for the demands of their sport. The future of ACL rehabilitation lies in embracing the complexity of the sensorimotor system and developing multidisciplinary interventions that promote beneficial neuroplasticity. Advances in neuroscience and technology, such as artificial intelligence, can lead to more personalized and effective rehabilitation programs. Recognizing and addressing the multifaceted nature of ACL injuries can significantly improve rehabilitation outcomes, ensuring athletes can safely and effectively return to their sports.

This paper proposes a position control method for low cost EGR valve system in automotive application. Generally, position control system using in automotive application has many restrictions such as cost and space, the mechanical structure of actuator implies high friction and large difference between static friction and coulomb friction. This large friction difference occurs the vibrated position control result when the controller uses conventional linear controller such as P, PI. In this paper, low cost position control method which can apply under the condition of high difference friction mechanical system. Proposed method is verified by comparing conventional control result of experiments.

Recently, various research has been developed addressing communication sensors for Unmanned Aerial Systems (UASs); especially when pondering control difficulties, communication is a crucial component. To this end, strengthening a control algorithm with redundant linking sensors ensures the overall system works accurately even if some components fail. This paper proposes a novel approach to integrate several sensors and actuators for a heavy Unmanned Aerial Vehicle (UAV). Additionally, a cutting-edge Robust Thrust Vectoring Control (RTVC) technique is designed to control various communicative modules during a flying mission and converge the attitude system to stability. The results of the study demonstrate that even though RTVC is not frequently utilized, it works as well as cascade PID controllers, particularly for multi-rotors with mounted flaps, and could be perfectly functional in UAVs powered by thermal engines to increase the autonomy since the propellers cannot be used as controller surfaces.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

This paper is concerned with the optimal linear quadratic Gaussian (LQG) control problem for discrete time-varying system with multiple input delays and multiplicative noises. The main contributions are two-fold. Firstly, when the state variables can be observed exactly, we obtain a necessary and suffcient condition for the multiple-delays system in terms of the non-homogeneous relationship between the state and costate, which is the solution to the coupled forward and backward stochastic difference equations. Secondly, when the state variables are partially observed, we derive a suboptimal linear output feedback controller for the discrete-time system based on the obtained results of the optimal LQG control. Numerical examples are shown to illustrate the proposed algorithm.

This article addresses the trajectory tracking between two non-identical systems with chaotic properties. We employ the Rossler chaotic and RCL-shunted Josephson junctions model in similar phase space to study trajectory tracking. In order to achieve the goal tracking, we afford two stages to approximate the target tracking. The first stage utilizes the active control technique to transfer the output signal from the RCLs-J system into the quasi-Rossler system. Then next, the RCLs-J system employs the proposed the iterative learning control scheme and the control signal from the drive system to trace the trajectory of Rossler system. The numerical results demonstrate the proposed method and the tracking system is asymptotically stable.

In this paper, modelling designing and simulation of a Ward Leonard layout system is done using robust control theory. In order to increase the performance of the Ward Leonard layout system with H infinity optimal control synthesis and H infinity optimal control synthesis via gamma-iteration controllers are used. The open loop response of the Ward Leonard layout system shows that the system needs to be improved. Comparison of the Ward Leonard layout system with H∞ optimal control synthesis and H infinity optimal control synthesis via gamma-iteration controllers to track a desired step speed input have been done. Finally, the comparative simulation results prove the effectiveness of the proposed Ward Leonard layout system with H infinity optimal control synthesis controller in improving the percentage overshoot and the settling time.

This paper addresses the input coupling problem for the shape memory alloy (SMA) actuated parallel platform with completely unknown nonlinear dynamics. The problem means that in a parallel platform with multiple SMA actuators acting together, the displacement of one of the actuators will simultaneously affect the remaining actuators to change. Due to the coupling problem and nonlinear characteristics of the SMA actuators, it is not easy to control the platform to achieve bi-directional swing with completely unknown nonlinear dynamics. Therefore, a model-free adaptive sliding mode control method is proposed to improve the control accuracy of the complex motion of the platform, avoiding the complex modeling process of the SMA actuated parallel platform. Based on the model-free adaptive control (MFAC) theory, a multiple input and multiple output (MIMO) dynamic linearized mathematical model of the parallel platform is established using the online input and output data of the system. Then, a sliding mode controller is designed based on the dynamic model. A rigorous theoretical analysis proves that the proposed model-free adaptive sliding mode controller is able to stably control the SMA actuated parallel platform, and its tracking error is uniformly ultimately bounded (UUB). Finally, experiments are conducted on the established SMA actuated parallel platform. The results show that the proposed model-free controller is feasible and effective, and can control the system well to achieve the complex motion of bi-directional swing.

Microgrids (MGs) have evolved as critical components of modern energy distribution networks, providing increased dependability, efficiency, and sustainability. Effective control strategies are essential for optimising MG operation and maintaining stability in the face of changing environmental and load conditions. Traditional rule-based control systems are extensively used due to their interpretability and simplicity. However, these strategies frequently lack the flexibility for complex and changing system dynamics. This paper provides a novel method called hybrid intelligent control for adaptive MG that integrates basic rule-based-control and deep learning techniques, including gated recurrent units (GRU), basic recurrent neural network (RNN) and long short-term memory (LSTM). The main target of this hybrid approach is to improve MG management performance by combining the strengths of basic rule-based systems and deep learning techniques. These deep learning techniques readily enhance and adapt control decisions based on historical data and domain-specific rules, leading to increasing system efficiency, stability, and resilience in adaptive MG. Our results show that the proposed method optimises MG operation, especially under demanding conditions such as variable renewable energy supply and unanticipated load fluctuations. This study investigates special RNN architectures and hyper-parameter optimization techniques with the aim of predicting power consumption and generation within the adaptive MG system. Our promising results show the highest-performing models indicating high accuracy and efficiency in power prediction. The finest-performing model accomplishes an R2 value close to 1, representing a strong correlation between predicted and actual power values.

This study introduces an advanced dual-mode composite attitude control framework for reusable launch vehicles (RLVs), underpinned by an enhanced particle swarm optimization (PSO) algorithm. This innovative strategy is tailored to meet the stringent demands for precision and robust anti-interference capabilities across the entire flight envelope of RLVs. The research commences with the formulation of a comprehensive attitude dynamics model and diverse heterogeneous actuator representations, meticulously crafted to reflect the distinct phases of RLV flight. Building upon this foundation, a synergistic control paradigm is engineered, integrating PID and fuzzy PID controllers, complemented by a refined fitness evaluation function. The crux of the study is the application of an upgraded PSO algorithm to fine-tune the controllers' weighting coefficients, culminating in an optimized dual-mode composite attitude control system. A series of comparative simulation analyses are meticulously executed to appraise the system's responsiveness, stability, precision, and resilience to interference. The findings unequivocally substantiate the superior performance of the proposed control system, affirming its substantial contribution to the advancement of RLV attitude control.

In a V2V communication environment, the control of electric vehicle platoons faces issues such as random communication delays, packet loss, and external disturbances, which affect sustainable transportation systems. In order to solve these problems and promote the development of sustainable transportation, a longitudinal control algorithm for the platoon based on robust Unscented Kalman Filter (UKF) and Model Predictive Control (MPC) is designed. First, a longitudinal kinematic model of the vehicle platoon is constructed, and discrete state-space equations are established. The robust UKF algorithm is derived by enhancing the UKF algorithm with Huber-M estimation. This enhanced algorithm is then used to estimate the state information of the leading vehicle. Based on the vehicle state information obtained from the robust UKF estimation, feedback correction and compensation are added to the MPC algorithm to design the robust UKF-MPC longitudinal controller. Finally, the effectiveness of the proposed controller is verified through CarSim/Simulink joint simulation. The simulation results show that in the presence of communication delay and data loss, the robust UKF-MPC controller outperforms the MPC and UKF-MPC controllers in terms of MSE and IAE metrics for vehicle spacing error and acceleration tracking error, and exhibits stronger robustness and stability.

The process model is very essential for the model based control design. The model of the process can be identified using system identification algorithm. The system identification is done through the open loop and closed loop approaches. In this work, the lab scale conical tank setup configured as a non square MIMO system. The conical tank system is identified through the both appraoches, the effectiveness and need of the both approaces are discussed. Based on the open loop identified model the controller designed and the controller implemented in the real time the to record the process data. From this data the closed loop identification are conducted uisng N4SID algorithm. The controller seetings are obtained using the smith predcitor based IMC based PI controller for the obtained model. The proposed identification algorithm and controller tuning show the better reults over the conventional method. Moreover, this method is applicable for all the non square MIMO system.

Modern power systems are confronted with a spread concern on the frequency stability issue due to the widespread integration of randomly fluctuating renewable resources. To address the above concern, this work introduces a load frequency control (LFC) scheme based on a hydrogen based energy-storage unit and a parameter tuning strategy for fractional-order proportional-integral-derivative (FOPID) controller. Firstly, a two-area interconnected power system (IPS) model, including thermal, hydro, solar, wind, gas power generator, and hydrogen based energy-storage unit, is established. Then, a FOPID controller is designed for this IPS model, and an improved gradient-based optimizer (IGBO) is developed to adaptively regulate the parameters of the FOPID controllers. Finally, the effectiveness of the offered LFC scheme are tested through load disturbance and renewable energy fluctuations test scenarios, and provides comparison and robustness analysis among different schemes. The test results validated that the offered LFC scheme can effectively suppress the frequency fluctuations of IPS and has excellent robustness.

The object of study in the article is the auxiliary variable-frequency electric drive onboard of an AC electric locomotive, and more precisely, the structure and parameters of the control system of the active corrector of power factor (ACPF), which is part of the electric drive, as a unit that ensures electromagnetic compatibility of the electric drive with other equipment on board the electric locomotive and catenary and its energy efficiency. The topic of the article's research is the substantiation of the structure of the ACPF control system, the choice of types (transfer functions) of controllers and the calculation of their parameters. The research method chosen was the calculation of the transfer functions of the controllers of the cascaded loops control system, followed by computer simulation using the OrCAD software. The results of simulating a cascaded two loops control system of an ACPF with different types of controllers are presented and analytically compared. It is concluded that it is advisable to use a cascaded two loops control system, containing a voltage control loop and a current control loop. The best results show the simulation when using an aperiodic current controller (A-controller) and a proportional-integral voltage controller (PI-controller). The data obtained serve as the basis for further improvement of the operation of the ACPF by enhancing and optimizing the settings, structure and algorithms of the control system, which, with the selected hardware of power converter unit, will allow the ACPF to perform its functions with a high degree of reliability in an entire range of disturbing and control influences.

Abstract: This work is presenting a water level control and monitoring system in a tank, by using the development boards Arduino Uno, NodeMCU ESP8266 and the ultrasonic sensor HC-SR04. The water tank is supplied through a pump controlled by the development board Arduino Uno by means of a relay. The author has issued a control algorithm for controlling the water level and a program in the development environment Arduino IDE that has been implemented and validated on the experimental stand. For monitoring the development board NodeMCU ESP8266 has been used that takes the information from the ultrasonic sensor HC-SR04 and sends it through Wi-Fi to the platform ThingSpeak. Based on the experimental results it has been found out that the water level control system in the tank provides the performances imposed through the designing stage, and the pump running has been automatically stopped when the limit, settled through the program for the water maximum level in the tank, has been reached. The measured value of the water level is shown on the LCD 1602 display. In the water circuit of the home consumers or irrigation systems supplied from the tank a pump controlled by means of a mobile phone has been installed. In order to implement this control system, the author has issued a program for another development board Arduino Uno, whilst for the mobile phone he used the application Arduino Bluetooth Control. The advantage of this water level control and monitoring system in the tank is given by the very low costs in comparison to the cost of an industrial type control system.

The medium-voltage DC (MVDC) technology has gained increasing interests in recent years. Power electronics devices dominate those grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system level control model of such a modern MVDC distribution grid. Aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/- 10 kV MVDC grid case study, including Solid State Transformer (SST) and Modular Multilevel Converter (MMC) based Active Front End (AFE). Additional aim of this paper is to supply an educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented.

A reinforcement learning control method for a solid attitude and divert propulsion system is proposed. The system in this research includes 4 divert thrust nozzles, 6 attitude thrust nozzles, and a common combustion chamber. To achieve the required thrust, the pressure in the combustion chamber is first adjusted by controlling the total opening of the nozzles to generate the gas source. Next, by controlling the opening of nozzles at different positions, the required thrust is produced in five-axis directions. Finally, the motor speed is regulated to drive the valve core to the specified position, completing the closed-loop control of the nozzle opening. The control algorithm used is the Proximal Policy Optimization (PPO) reinforcement learning algorithm. Through system identification and numerical modeling, the training environment for the intelligent agent is created. To accommodate different training objectives, multiple reward functions are implemented. Ultimately, through training, a multi-layer intelligent agent architecture for pressure, thrust, and nozzle opening is established, achieving effective system pressure and thrust control.

To mitigate the interference of waves on offshore operation vessels, heave compensation systems find widespread application. The performance of heave compensation systems significantly influences the efficiency and safety of maritime operations. This paper establishes a mathematical model for winch-based active heave compensation system. It introduces a three-loop active disturbance rejection control (ADRC) strategy encompassing piston position control, winch speed control, and load displacement control, enabling real-time estimation and compensation of system disturbances, thereby enhancing the performance of the heave compensation system. To assess the effectiveness of this control strategy, the study employs Matlab/Simulink and AME Sim to construct a co-simulation model and conducts a comparative analysis with traditional PID control systems. The research findings indicate that the three-loop ADRC position control strategy consistently delivers superior compensation performance across various operational scenarios.

In this paper, an adaptive proportional and integral (PI) controller for speed control of electric drives is developed. The adaptive PI controller is designed based on model reference adaptive identification and the Lyapunov function so that the system stability can be proved. To identify the inertia, the viscous friction torque coefficient, and the load torque simultaneously and smoothly, sinusoidal speed reference is usually employed. A modified motion equation considering the low-pass filter for feedback speed and the Coulomb friction torque is introduced and the relationship between the identified parameters and their actual values are clarified. A high-precision digital simulation model is built based on MATLAB/Simulink. The effectiveness of the proposed method is verified by simulations under various conditions.

Standard Ethernet (IEEE 802.3 and the TCP/IP protocol suite) is gradually applied in industrial control system (ICS) with the development of information technology. It breaks the natural isolation of ICS, but contains no security mechanism. A modified intrusion detection system (IDS), which is strongly correlated to specific industrial scenario, is necessary for modern ICS. On the one hand, this paper outlines attack models, including infiltration attacks and our creative forging attack. On the other hand, we proposes a hierarchical IDS, which contains a traffic prediction model and an anomaly detection model. The traffic prediction model, which is based on autoregressive integrated moving average (ARIMA), can forecast the traffic of ICS network in the short term and precisely detect the infiltration attacks according to abnormal changes in traffic pattern. The anomaly detection model using one-class support vector machine (OCSVM) is able to detect malicious control instructions by analyzing the key field in EtherNet/IP packets. The experimental results show that the hierarchical IDS has an outstanding performance in detecting infiltration attacks and forging attack compared with other two innovative IDSs.

DC residential distribution system (RDS) consisted by DC living home will be a significant integral part in the future green transmission. Meanwhile, the increasing number of distributed resources and intelligent devices will change the power flow between main grid and demand sides. The utilization of distributed generations (DGs) requires an economic operation, stability, environmentally friendly in the whole DC system. This paper not only presents an optimization schedule and transactive energy (TE) approach through centralized energy management system (CEMS), but a control approach to implement and ensure DG output voltages to various DC buses in DC RDS. Based on data collection, prediction and a certain objection, the expert system in CEMS can work out the optimization schedule, after this, the voltage droop control for steady voltage is aligned with the command of unit power schedule. In this work, a DC RDS is as a case study to demonstrate the process, the RDS is associated with unit economic models, cost minimization objective is proposed to achieve based on real-time electrical price. The results show that the proposed framework and methods will help the targeted DC residential system to reduce the total cost and reach stability and efficiency.

The problem addressed in this article consists of the motion control of a quadrotor, comprising model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor build from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on.

Previous control methods developed by our research team cannot satisfy the high-accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) guidance law for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle, with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain a high-accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS guidance law can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, and reduce the average cross-track error by 10.91%, and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional method.

The paper addresses the challenging issue of trajectory tracking for uncertain quadrotor UAVs, particularly under the constraints of input saturation and external disturbances. It introduces adaptive laws for managing uncertainties in mass and inertia moments without requiring prior knowledge, ensuring effective control even with varying system parameters. To counteract the effects of input saturation, the study incorporates an auxiliary system designed to compensate for these limitations. Additionally, a disturbance observer(DO) is utilized to manage and mitigate the impact of time-varying external disturbances. The proposed control strategy integrates a sliding mode adaptive control approach with an inner-outer loop structure, enhancing robustness and adaptability. Numerical simulations demonstrate the effectiveness of the designed control strategy.

This paper describes how to construct a low-cost magnetic levitation system (MagLev). The MagLev has been intensively used in the engineering education, allowing instructors and students to learn through hands-on experiments essential concepts, such as electronics, electromagnetism, and control systems. Built from scratch, the proposed MagLev depends only on simple, low-cost components readily available on the market. In addition to showing how to construct the MagLev, this paper presents a semi-active control strategy which seems novel when applied in the MagLev. Experiments performed in the laboratory have compared the proposed control scheme with the classical PID control. The corresponding real-time experiments illustrate both the effectiveness of the approach and the potential of the MagLev for education.

In this work, an energy-saving smart light controlling system has been proposed that can main-tain the desired intensity of light in a room automatically. Unlike the conventional light control system, the proposed system splits a large room into several zones and analyzes the light inten-sity of each zone; hence, the controlling unit adjusts the light intensity to the desired level. The main controlling unit consists of a light sensor, a motion sensor, a relay with a driver unit, an LCD display, etc. for controlling light efficiently to reduce the power waste. The sensors meas-ure the intensity of light, based on the standard light intensity data chart the controller units make a decision how many light bulbs are needed to be switched ON/OFF in a particular zone. Moreover, the system automatically switched-OFF all light bulbs when there is nobody in the room. Proteus design suite 8.0 is used to design and simulation of the proposed system. Moreo-ver, the PCB layout is designed using ExpressPCB version 7.5.0. The proposed system is capable of minimizing the power loss by up to 44% in comparison to the conventional light manage-ment system.

Recently, design of electric scooters (ESs) has commonly adopted brushless DC motors (BLDCMs) in place of brushed DC motors. This invention develops a new anti-lock braking system (ABS), based on a slip-ratio estimator, for ES utilizing the braking force generated by the BLDCM when electrical energy releases to the load yielding an analogous effect of ABS control in gas-engine vehicles. Comparing to mechanical ABS, the design possesses the advantages of rapid torque responses due to fast actuating response. The electrical ABS is realized by associating with kinematic and Short-circuit braking. A current controller is used to adjust the braking force, while the sliding mode control strategy is adopted to regulate the slip ratio for best road adhesion while braking. Real-world experiments have been conducted for functional and performance verification.

The uncertainty in the secondary path of an Active Noise Control (ANC) headphone affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones, and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized by the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensures more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

This paper is an extension to previous work that introduced successful classical-control for indoor lighting system that simultaneously control LED system and roller curtain to achieve accurate light intensity for indoor lighting applications. In this paper, Multi Input Multi Output Fuzzy Logic Controller (MIMO FLC) is proposed to replace the classical-controller. Detailed method is provided to illustrate the steps to design the required MIMO FLC that has two fuzzy input, two fuzzy output and forty-five fuzzy rules with mamdani fuzzy engine. The proposed MIMO FLC is simulated in Matlab using Simulink tools. The results are illustrated, analyzed and compared with the classical-control. The response showed very close performance to the classical-controller. The maximum average error and the maximum error with respect to “Desired Lux” inside the room is -6% and -10% respectively.

In an effort to upgrade and provide a unified and improved instrument control and data acquisition system for the ORNL SANS instrument suite (Bio-SANS, EQ-SANS, GP-SANS), beamline scientists and developers teamed up and worked closely together to design and develop a new system. We began with an in-depth analysis of user needs and requirements, covering all perspectives of control and data acquisition based on previous usage data and user feedback. Our design and implementation were guided by the principles from the latest user experience and design research and based on effective practices from our previous projects. In this article, we share details of our design process as well as prominent features of the new instrument control and data acquisition system. The new system provides a sophisticated Q-Range Planner to help scientists and users plan and execute instrument configurations easily and efficiently. The system also provides different user operation interfaces, such as wizard-type tool Panel Scan, a Scripting Tool based on Python Language, and Table Scan, all of which are tailored to different user needs. The new system further captures all the metadata to enable post-experiment data reduction and possibly automatic reduction and provides users with enhanced live displays and additional feedback at the run time. We hope our results will serve as a good example for developing a user-friendly instrument control and data acquisition system at large user facilities.

Battery Management System helps to improve the transient performance and life of electro-chemical rechargeable batteries like a Li-ion, lead–acid, etc. The investigation here was focused on the crucial part of the Battery Management System that is, Cell balancing, State of charge of the battery, etc. By analyzing the different control strategies a new optimal control strategy is developed to improve the battery performance according to application. The Control strategy is developed using an active and passive cell balancing model with the help of AVL Cruise and MATLAB/SIMULINK software. For the active cell balancing technique, a switched capacitor technique is chosen. To have longer battery life, the time required for the cell balancing must be as low as possible for which many control logics have been defined. The effect of these cell balancing techniques on the performance of battery packs is investigated and it is observed to produce a promising result.

Around 14% of the global population does not have access to electricity. About 95% of those are living in rural Sub-Saharan Africa. Often in these regions, diesel generators are the only source of electricity. The operating cost of these diesel generators is high. However, solar and wind energy are available in most of African countries. This study presents the analysis of designing an off-grid hybrid system with a wind turbine, PV, diesel generator, and battery to power a hospital, school, and 200 household village in four locations across Somalia. The research investigated the availability of wind-solar resources in selected locations. Designing of the system and economic-technical calculations were performed using HOMER. The selection of the optimum design was based on the Cost of Electricity and Net Present Cost. The results show that for Kabaal and Ceel Buur, a WT-PV-DG-Battery is the optimal system as the wind resource in these regions is high. For Saakov and Baki, a PV-DG-Battery system proves to be optimum as the wind resource is limited here. The study also evaluated the control strategy and proved that combined dispatch was the most cost-effective for these locations. The study concluded that hybrid systems are more economical than diesel systems.

This paper examines the control challenges faced by underactuated Autonomous Underwater Vehicles (AUVs) under ocean current disturbances. It proposes a Backstepping Integral Sliding Mode Control (BISMC) strategy to enhance their adaptability and robustness. The BISMC strategy integrates the system decomposition capability of the Backstepping Control method with the rapid response and robustness advantages of the Sliding Mode Control method, enabling the design of a heading controller and a double closed-loop depth controller. By introducing an integral component, the strategy eliminates steady-state errors caused by ocean currents, accelerating system convergence and improving accuracy. Furthermore, a saturation function is employed to mitigate output chattering issues. Simulation results demonstrate that the BISMC controller significantly enhances the control precision and anti-disturbance capabilities of AUVs under low-frequency ocean current disturbances, showcasing exceptional adaptive and self-disturbance rejection performance.

The increasing complexity of vehicular traffic and the prevalence of accidents, particularly rear-end collisions, necessitate enhancements in automotive safety technologies. Adilights, an innovative automotive lighting system, addresses this need by introducing a dynamic visual communication method between vehicles. This system features a novel design that employs variable-intensity brake lights configured in an upside-down "U" shape to indicate braking severity. The intensity and pattern of the lights change in response to the pressure applied to the brake pedal, providing clear and immediate feedback to following drivers. This design not only improves the perceptibility of braking signals, particularly in dense traffic and poor visibility conditions but also aims to reduce the frequency of rear-end collisions significantly. The paper details the development and testing of Adilights, discusses its integration with current vehicular systems, and evaluates its effectiveness through simulated scenarios. The introduction of Adilights marks a step forward in the use of adaptive lighting systems for enhanced vehicular communication and road safety.\cite{Lee2007RearEnd}

The dairy industry relies heavily on milk pasteurization to ensure the safety and quality of milk products. Pasteurization is a process that involves heating milk to a specific temperature for a set period of time to kill harmful bacteria and other pathogens while preserving its nutritional value. recent years, newer techniques such as High Temperature Short Time (HTST) pasteurization have gained popularity due to their efficiency and effectiveness. HTST pasteurization involves rapidly heating milk to a high temperature for a short duration of time, followed by rapid cooling. This process helps to minimize the loss of flavor and nutrients in milk while ensuring its safety. However, there is still a need in the market for a compact and remotely controlled process plant for milk pasteurization. The proposed work aims to address this need by designing a process plant that is not only compact but also remotely controlled, allowing for efficient and convenient operation. can control the process plant remotely, allowing for flexibility and convenience in monitoring and managing the pasteurization process from anywhere with an internet connection

Observer-based control is the most commonly used method in the control of electro-hydraulic servo system (EHSS) with uncertainties, but it suffers from the drawback of low accuracy under the influence of large external load forces and disturbances. To address this problem, this paper proposes a novel compensation function observer-based backstepping sliding mode control (BSMC) approach to achieve high-accuracy tracking control. In particular, the model uncertainties, including nonlinearities, parameter perturbations and external disturbances are analyzed and treated together as a lumped disturbance. Then, a fourth-order compensation function observer (CFO) is constructed, which fully utilizes the system state information to accurately estimate the lumped disturbance. On this basis, the estimate of the lumped disturbance is incorporated into the design of an backstepping sliding mode controller, allowing the control system to compensate for the disturbance effect. The stability of the closed-loop control system under the CFO and BSMC is rigorously proven through the use of Lyapunov theory, which guarantees that all the tracking error signals converge exponentially to the origin. Comparative simulations are carried out to show the effectiveness and efficiency of the proposed approach, i.e., compared with PID and ESO-based BSMC methods, the tracking accuracy is improved by 99.93% and 99.76%, respectively.