This paper introduces a structured design for an effective power-sharing technique among converter-interfaced distributed generation (DG) units within a microgrid that operates without a synchronous generator. The proposed power-sharing technique leverages the battery energy storage system (BESS) to promptly respond to network changes. The real power output of each commercial distributed energy resources (CDER) unit is governed by a frequency-droop characteristic and a complementary frequency restoration strategy. Deloading techniques are employed by solar and wind power generators to enhance response during power supply or demand disturbances. To ensure microgrid stability, small-signal analysis is conducted for controller design, including the determination of stability margins. Initial tuning of the power-sharing technique parameters is achieved using the Ziegler-Nichols Method, followed by further optimization through a meta-heuristical algorithm to enhance the response time of energy sources. The proposed power-sharing technique's performance is evaluated on a benchmark medium voltage network using industry-standard commercial software. The test results demonstrate the precise and rapid power-sharing capabilities among DGs facilitated by the proposed technique, highlighting its effectiveness in dynamic microgrid environments.

This paper aims at achieving optimal rotor speed estimation for an induction motor using the Extended Kalman filter (EKF). Speed estimation is essential for fault diagnosis in Motor Current Signature Analysis (MCSA). The estimation accuracy is obtained by exploring the noise covariance matrices estimation of the EKF algorithm. The noise covariance matrices are determined using a modified subspace model identification approach. In order to reach this goal, this method compares an estimated model of a deterministic system, derived from available input-output datasets, with the discrete-time state-space representation used in the Kalman filter equations. This comparison leads to the determination of model uncertainties, which are subsequently represented as noise covariance matrices. Based on the 5th order nonlinear model of the induction motor, rotor speed is estimated with the optimized EKF algorithm and the used algorithm is tested experimentally.

This article explores the implementation of high accuracy GPS denied Ad-Hoc localization. There is little research on Ad-Hock UWB-enabled localization systems with mobile and stationary nodes. This work aims to demonstrate the localization of bicycle-modeled robots in a non-static environment through a mesh network of mobile, stationary robots and ultra-wideband sensors. The non-static environment adds a layer of complexity when actors can enter and exit the node’s field of view. The method starts with an initial localization step where each unmanned ground vehicle (UGV) uses the surrounding, available anchors to derive an initial local or, if possible, global position estimate. The initial localization uses a simplified implementation of the iterative multi-iteration Ad-Hoc Localization System (AHLos). This estimate was refined using an unscented Kalman filter (UKF) following a constant turn rate and velocity magnitude model (CTRV). The UKF fuses the robot’s odometry and the range measurements from the Decawave ultra-wideband receivers stationed on the network nodes. Through this position estimation stage, the robot broadcasts to its neighbors its estimated position to help the others further improve their localization estimates and localize themselves. This wave-like cycle of nodes helping to localize each other allows the network to act as a mobile Ad-Hoc localization network.

Usually, the vector control of the induction machine (IM) is done by using cascade control structures with conventional linear PI controllers, the inner loop being designed for current control, and the outer loop for rotor flux and speed control. In this paper, starting from the dq model of the IM with the rotor magnetic flux space vector aligned along the d axis, advanced control algorithms are proposed for the two control loops of the cascade structure. For the current inner loop, after the decoupling control of the two dq currents, predictive algorithms with constraints are used to control and limit the currents. Since the outer loop has a nonlinear affine multivariable plant model, a homotopy-based variant of feedback linearization is used to obtain a non-singular decoupling matrix of the feedback transformation even when the rotor flux is zero at the start-up of the motor. During the continuous variation of the homotopy parameter, the model of the plant is variable, in which case the model free algorithms are properly used to control the flux and speed of the IM. The comparative analysis obtained for the cascade structure with conventional PI controllers and advanced algorithms, respectively, shows that the last lead to improve the dynamical performance of the IM.

The human-centric approach is a leading trend for future production processes, and collaborative roboticists are key to its realization. This article addresses the challenge of designing a new cus-tom-made non-conventional machine or robot involving toolpath control (interpolated axes) with collaborative functionalities but by using “general-purpose standard" safety and motion control technologies. This is done on a non-conventional Cable-Driven Parallel Robot (CDPR). Safety is assured by safe commands to individual axes, known as Safe Motion Monitoring Functionalities, which limits the axes speed in the event of human intrusion. At the same time, the robot's Motion Controller applies an override to the toolpath speed to accommodate the robot's path speed to the axis’s limitations. The implementation of a new Pre-Warning Zone, prevent unnecessary stops due to the approach of the human operator. The article also details a real experiment that validates the effectiveness of the proposed strategy.

Smart home and building systems are popular solution that support maintaining comfort, safety and improving energy efficiency in buildings. However, dynamically developing distributed network technologies, in particular the Internet of Things, are increasingly entering the above-mentioned application areas of building automation, offering new functional possibilities. The result of these processes is the emergence of many different solutions that mix technologies of field-level and ICT networks in various configurations and architectures. New paradigms are also emerging, such as edge and fog computing, providing support for local monitoring and control networks in the implementation of advanced functions and algorithms, including machine learning and artificial intelligence mechanisms. This paper collects state-of-the-art information in these areas, along with a systematic review of the literature, case-studies and an analysis of selected development trends. The author systematized this information in the context of the potential development of building automation systems. Based on the conclusions of this analysis and discussion, a framework for the development of the Generic IoT paradigm in smart home and building applications has been proposed, along with a SWOT analysis of its usability. Future works are proposed as well.

Adjustable frequency converters are widely used in modern automated asynchronous drives due to their high efficiency and high responsiveness control strategies. Control algorithms for frequency converters are divided into two main groups: constant frequency commutation and random frequency commutation. Research results on various commutation frequencies, conducted using Matlab/Simulink software, show that the use of random frequency commutation provides better efficiency by significantly reducing the amplitude of harmonic oscillations at different frequencies, with a reduction in switching losses in transistors. Currently, a converter with random frequency commutation is being researched and applied in the direct torque control system. The traditional direct control system, based on a two-level inverter, is widely used due to its simplicity and ease of implementation. However, this method results in the output current of the two-level converter, supplied to the motor stator, having many harmonic distortions, leading to strong torque fluctuations regardless of the rotation speed and ultimately causing additional losses in the stator windings and reducing the motor's lifespan. To enhance the efficiency of automated asynchronous drives, this article presents an algorithm for a direct control system based on a three-level NPC inverter using a PI speed regulator. The proposed algorithm's research is validated in the Matlab/Simulink environment. The research results show that in the absence of harmonic filtering, the stator current harmonic distortion coefficient is approximately 11%, and torque fluctuations are less than 5% of the resistive torque. The fluctuations between the two capacitors remain almost unchanged throughout the entire process.

This paper proposes the application of force-projecting bilateral control to a master-follower teleoperation system with a pneumatic manipulator on the follower side, and evaluates its effectiveness. The proposed method directly projects the operating force on the master side to the driving force on the follower side, eliminating the need for both position control and external force detection on the follower side, thereby solving the problem of low rigidity and response delay of pneumatic manipulators and providing highly stable sensor-less force presentation against variable environments. In this study, dynamic response analyses of a 1-DOF master-follower system were performed by numerical simulation using a linear system model, followed by experimental verification by implementing an actual system with an external force estimator. The results showed that the proposed force-projecting bilateral control has significantly higher positioning rigidity and better force control stability than the conventional force-reflecting bilateral control. A theoretical consideration was also given using equivalent transformation of force transfer functions to provide evidence for the high stability.

This paper introduces a fuzzy logic-based autonomous ship-deck landing system for fixed-wing Unmanned Aerial Vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship-deck landing model is based on a fuzzy system comprised of three interconnected subsystems. Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship-deck landings. Specifically, the Fuzzy Logic System (FLS) takes inputs such as the airspeed, the relative wind direction and speed, the range from the deck and the UAV's flight altitude. The FLS outputs provide data on the Speed of the UAV relative to the ship’s velocity, the Bank Angle, and the Angle of Descent of the UAV. The performance of the designed intelligent ship-deck landing system is evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

An event-based dynamic output feedback control (DOFC) strategy for unmanned marine vehicle (UMV) systems is concerned. The whole UMV systems are composed of an UMV closed-loop system, a land-based control unit and the communication network. To increase the effectiveness of data transmission in the network channel and better enable the control unit against attack, an adaptive event-triggered mechanism (AETM) is applied. Moreover, a quantizer is installed between the sampler and the control unit. The quantizer further reduces the communication burden. The occurrence of aperiodic denial-of-service (DoS) attack is considered in the channel from the control unit to the UMV system. Sufficient criterion for ensuring global exponential stability of system with an expected H∞ disturbance attenuation index is obtained. Co-design of the dynamic output feedback controller and the AETM is derived. The effectiveness of the proposed approach is verified in the given illustrative simulation. The simulation results indicate that the reduction percentages of yaw angle amplitudes and yaw velocity accumulative error of the UMV system with the control strategy proposed in this paper are 43.2%and 45.9%, respectively, which are 0.3% and 5.8% improvement in both metrics compared to the previously published work.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations

Optimal operation of petroleum production is important in a transition from energy systems based on fossil fuel to sustainable systems. One sub-process in petroleum production deals with transport from the (subsea) well-bore to a topside separator. A simple model in [] of Electric Submersible Pump [ESP] lifted production was previously streamlined into a dynamic model suitable for illustration of the dynamics of oil transport, as well for control studies, with some comparison of two popular modeling languages: Modelica, and ModelingToolkit for Julia, []. Here, the discussion on dimensionless equipment models goes into more detail, and the comparison between Modelica and ModelingToolkit is significantly expanded upon with code comparison, numerical performance, and more experiments. Some added possibilities with ModelingToolkit and Julia wrt. sensitivity analysis and control design is included.

The paper deals with the Model Predictive Control (MPC) algorithm as applied to the sensorless control of a Permanent Magnet Synchronous Motor (PMSM). The proposed estimation strategy, based on the unscented Kalman filter (UKF), uses only the measurement of the motor current for the online estimation of speed, rotor position and load torque. Information about the system state feeds the MPC. Results verify the effectiveness and applicability of the proposed sensorless control technique. An implementation in low speed direct drive astronomy telescope mount systems is investigated.

Automated Guided Vehicles are self-guided vehicles used in manufacturing and warehouses for material transport. They play a vital role in just-in-time material delivery, ensuring smooth production lines. However, disruptions in their fixed routes can lead to efficiency losses. This paper studies unexpected disruptions in mobile robot routes, assessing the costs of rectification using the E* analysis. The study highlights the significant additional cost of such disruptions, underscoring the importance of their consideration in AGV route planning to enhance system resilience.

This paper details a design optimization on a hardware accelerator for an on-board real-time SAR imaging system using the Backprojection algorithm, focusing on algorithm-specific approximations intended to reduce the overhead introduced by intensive functions such as the square root, sine, and cosine functions. The main novelty of this work is that new approximations were investigated so that the final image retains high quality regardless of the error in the approximation function. This paper revisits existing approximation methods, such as linear interpolation, polynomial approximation using the Vandermonde matrix, and Chebyshev polynomials, and compares their performance on the algorithm. Results demonstrate that it is possible to maintain the image quality with an SSIM above 0.99 with less 93% LUTs for the square root and less 88% LUTs for the sine and cosine functions using polynomial approximation with the Vandermonde matrix when compared to the HLS baseline functions.

Discrete-time architectures offer a distinct advantage over their continuous counterparts, as they can be seamlessly implemented on embedded hardware without the necessity for discretization processes. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based frameworks, are highly complex. As a result, various existing continuous-time results using adaptive control methods to deal with system uncertainties and coupled dynamics in agents of a multiagent system cannot be directly applied to the discrete-time context. Furthermore, compared to their continuous-time equivalent, discrete-time information exchange based on periodic time intervals is more practical in the control of multiagent systems. In this paper, we first introduce a discrete-time adaptive control architecture designed for uncertain scalar multiagent systems without coupled dynamics as a preliminary result. We then introduce another discrete-time adaptive control approach for uncertain multiagent systems in the presence of coupled dynamics. Our approach incorporates observer dynamics to manage unmeasurable coupled dynamics, along with a user-assigned Laplacian matrix to induce cooperative behaviors among multiple agents. Our solution includes Lyapunov analysis with logarithmic and quadratic Lyapunov functions for guaranteeing asymptotic stability with both controllers. To demonstrate the effectiveness of the proposed control architectures, we provide an illustrative example.

In this paper, experimental results about the performance of a Takagi-Sugeno Fuzzy Controller (TSFC) for an EV3 Ballbot Robotic System (EV3BRS) are reported. The physical configuration for the EV3BRS has the form of an inverted pendulum mounted on a ball. The EV3BRS is an underactuated robotic system with four outputs and two control torques. The Takagi-Sugeno Fuzzy Model (TSFM) design comes from linearization of the nonlinear model around two operation points near from the upright position of the EV3BRS's body. The Parallel Distributed Compensation (PDC) approach is used to the design of the TSFC. The Linear Matrix Inequalities (LMIs) approach is used to get the feedback gains for every local linear controller guaranteeing global asymptotically stability, via a more relaxed stability condition, of the overall fuzzy control system. Measurement and control data from and to the EV3BRS are transferred via telecontrol and telemetry.

An advanced driver-assistance system (ADAS) is critical to driver-vehicle-interaction systems. Driving behaviour modelling and control significantly improves the global performance of ADAS. A haptic assistive system assists the driver by providing a specific torque on the steering wheel according to the driving-vehicle-road profile to improve the steering control. However, the main problem is designing a compensator dealing with the high-level uncertainties in different driving scenarios with haptic driver assistance, where different personalities and diverse perceptions of drivers are considered. If not properly accounted for, these differences can lead to poor driving performance. This paper focuses on designing a data-driven model-free compensator considering various driving behaviours with a haptic feedback system. A back propagation neural network (BPNN) models driving behaviour based on real driving data (speed, acceleration, vehicle orientation, and current steering angle). Then using the genetic algorithm (GA) and the integral time absolute error (ITEA) criterion to optimize multiple PID compensation parameters for various driving behaviour (such as speeding/braking, lane-keeping and turning), which are then be combined by the fuzzy logic to provide different driving commands. An experiment was conducted with 5 participants in a driving simulator. During the second experiment, seven participants drove in the simulator to evaluate the robustness of the proposed combined GA-PID offline and the Fuzzy-PID controller applied online. The experiment results evaluated the ITAE of lateral displacement and yaw angle, during various driving behaviour. The results validated the proposed method by significantly enhancing the driving performance.

The purpose of water treatment through ultraviolet (UV) radiation is to interrupt the development and multiplication of microorganisms by preventing their growth and reproduction.Ultraviolet treatment has become a very affordable solution to produce clean water of optimal quality in regions where access to drinking water is essential. The experimental water treatment unit is powered by a photovoltaic (PV) source. It incorporates a discharge lamp used to generate ultraviolet radiation, which is harnessed to address bacteriological issues related to water quality. This lamp is continuously powered by a photovoltaic (PV) source through an inverter and an electronic ballast. The water treatment system also includes a motor pump designed to draw contaminated water from the input reservoir, a flow control valve to adjust flow rates as needed, and a filter aimed at enhancing the quality of the contaminated water. The water circulates between the input reservoir (contaminated water) and the output reservoir (treated water) through the UV lamp. The relationship between the intensity of the UV lamp and the water flow rate can ensure an adequate water quality.

This paper presents the mathematical modeling for designing a 10[kW] DC-AC inverter system from 48 [V] DC to 120 V ACrms, considering its non-linear operation, allowing to obtain a detailed simulation to assess the proposed fuzzy PI control (with anti-windup action) and its optimal operation. The performance assessment of the converter was carried out with the discrete treatment of the voltage and current signals using spectral analysis, filtering considerations, and evaluation of limit cycle technique to determine the system's stability. The main results showed that THD at the filter output (transformer and capacitor) was around 0.0322% and 0.0632% at nominal load in open loop and closed loop, respectively. The inverter system presented more sensitivity in the current signal with a ripple at 120 [Hz] of 2.02% and at 5000 [Hz] of 10.62% (at nominal load). However, when the system operates in a closed loop, the current ripple at 120 [Hz] is 2.38% and at 5000 [Hz] is 10.62%.

A paddle wheel aerator is developed in this research, which includes a wireless sensor network for measuring the water quality in the aquaculture of marine shrimp ponds. It can also move to the desired position to fill oxygen and measure water quality. A basic requirement is good standard water quality to prevent shrimp from epidemics and improve production. The water quality paddle wheel aerator applies a microcontroller and sensors to measure eight parameters of water quality. These water quality parameters are observed on the web application via an IoT module. The movement system of the water-quality paddle wheel aerator consists of a LiDAR, GPS, and remote RF signal. Each item of data is recorded immediately on the cloud server while the water-quality paddle wheel moves in the marine shrimp farm. The controlled paddle wheel aerator is harnessed automatically to enhance precisely the spatial monitoring resolution of the measurement system installed, which is needless for a multiple measurement system with high cost of investment. Also, farmers can access real data through the Line application. Hence, they are able to plan and control a good environment for aquaculture, preventing the occurrence of various epidemics and decomposing organic matter in the pond.

This paper addresses a mode-dependent state-feedback H∞ control for descriptor hybrid systems, considering both the absence and presence of actuator saturation. Firstly, the necessary and sufficient conditions for the stochastic admissibility criterion with H∞ performance ? of the closed-loop system is proposed. Given the proposed non-convex condition, the author reformulates it into linear matrix inequalities (LMIs). Then, to extend the result to the systems with actuator saturation, the actuator saturated control input is expressed as a linear combination of given state-feedback control input and a virtual control input that remains under the saturation level. To verify this expression, the set invariant condition is also suggested by using the singular mode-dependent Lyapunov function candidate. Therefore, the LMI conditions for the existence of the mode-dependent state-feedback H∞ control and the ellipsoidal shape of invariant set are successfully derived. Two numerical examples demonstrate the effectiveness of the proposed approach in optimizing H∞ performance $\gamma$ and identifying the largest invariant set.

This paper describes RTMN 2.0 - an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. The reusability and intuitive robot programming allows people without programming expertise to plan and control robots. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modelling language, RTMN 2.0 concentrates on Human Robot Collaboration (HRC), which represents the current trend of the industry shift from ‘mass-production’ to ‘mass-customization’. While traditional robot systems provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity. Therefore, human-robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in industrial scenarios. HRC has made a great impact on the robotic industry: increased efficiency, reduced costs and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension in RTMN 2.0 includes the following notations: HRC tasks, requirement, Key Performance Indicator (KPI), condition check and decision making, join/split and data association. With all these additional elements RTMN 2.0 covers the full scale of agile manufacturing – light out and collaborative.

The integration of robotics and image processing has led to the realization of robot autonomy in dynamic environments through the provision of visual feedback. This paper presents the application of parallel and open-link robots in palletizing and shape drawing tasks as enhanced by visual feedback from image processing. In determining the set of joint angles that could be used to reach the desired position and orientation of the end effector, the geometric approach in which the spatial geometry of the robotic arms was decomposed into several plane geometry problems was employed. Image processing techniques were used to enhance the performance of the robotic manipulators. In one approach, Color-based segmentation was used to distinguish between different objects in the workspace by using predefined color markers as references in the L*a*b color space. Classification of each pixel in the workspace image was then done by calculating the Euclidean distance between that pixel and a predefined color marker. A second approach employed Edge detection to identify the boundaries of objects within the workspace image by employing the Hough Transform mathematical model to detect the abrupt changes in the image brightness pixel-wise. The pixel locations from Hough were then sorted sequentially to outline the detected object. The integration of image processing with the robotic tasks was expected to improve the precise detection of the position of objects as well as the outline of geometric shapes. The incorporation of visual feedback allowed for dynamic robot manipulation in which prior knowledge of the workspace was not requisite. This led to improved pick and place as well as shape detection as applied in palletizing and shape drawing tasks actuated by the parallel and serial link manipulators, respectively.

This paper introduces the implementation of a genetic programming (GP)-based procedure to the automatic design and tuning of process controllers. The proposed approach makes a significant contribution to the field of artificial intelligence (AI) in control engineering. Unlike other controller design methods, the GP-based program handles the entire design in the time domain, including differential operations like derivatives and integrals, without the need for intermediate inverse Laplace transformation. This approach not only simplifies the design process but also ensures that all generated controllers are implementable in physical systems. Furthermore, GP’s functions set includes various mathematical operations beyond basic arithmetic operators, such as trigonometric, exponential, and logarithmic operators. The performance and validity of the resulting controllers generated by the proposed GP-based approach are evaluated by verifying whether the generator can replicate the structure and performance of those produced by traditional controller design methods and, in some cases, achieve even better results. As a result, the GP-based approach presents a promising solution for automating the controller design process and addressing control problems in various engineering applications.

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. While a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous-driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming inputs characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming inputs characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming inputs features, we introduce a backbone switcher based on the identified streaming inputs characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming inputs features.

Automatic generation control (AGC) plays a vital role in creating an equilibrium between generated output power and load demand, in order to maintain frequency at desired value. This study focuses on performance analysis of simple-multi attribute rating technique (SMART) assisted proportional-integral-derivative (PID) controller design for AGC of two interconnected power systems. PID controller is designed by minimizing frequency variations, area control errors for area-1 and area-2, and tie-line power deviation. An objective function is framed considering error minimization of aforementioned factors as sub-objectives. The construction of objective function involves using the integral of time-multiplied absolute error (ITAE) for frequency deviations for area-1 and area-2, ITAE for deviation in tie-line power, and ITAEs for area control errors for area-1 and area-2 as sub-objective functions. By assigning appropriate weights to these sub-objective functions, an overall objective function is formed. This study determines these weights in an organized manner using SMART method, rather than randomly/equally assigning them. Overall objective function is minimized using Jaya algorithm. To demonstrate effectiveness of the proposed Jaya-based PID controller, its performance is analysed and compared with controllers tuned using other optimization algorithms, including sine cosine, Luus-Jaakola, teacher-learner based optimization, Nelder-Mead simplex, and elephant herding optimization. Considering six different case studies that consider a range of load variations, responses for fluctuations in frequency and tie-line exchange are plotted. Statistical and non-parametric analysis performed further provide additional insights into the performance of Jaya-based PID controller.

Kinematic control of redundant serial manipulators has been carried out for the past half century based primarily on a generalized inverse velocity formulation that is known to have mathematical deficiencies. A recently developed inverse kinematic position mapping is extended to an operational configuration space differentiable manifold formulation for kinematic control with obstacle avoidance is employed to resolve deficiencies in the generalized inverse velocity formulation. Output trajectory tracking with obstacle avoidance for four and twenty degree of redundancy manipulators is carried out to demonstrate the effectiveness of the differentiable manifold approach and that it resolves deficiencies of the conventional generalized inverse velocity formulation.

This research investigates the compatibility of conventional air conditioning with the principles of green building, highlighting the need for systems that enhance indoor comfort while aligning with environmental sustainability. Conventional cooling systems, though proficient in regulating indoor temperatures, encounter several issues when incorporated into green buildings. These include energy waste, high running costs, and a misalignment with eco-friendly practices, which may also lead to detrimental environmental effects and potentially reduce occupant comfort, particularly in retrofit situations. Given the emphasis on sustainability and energy conservation in green buildings, there is a pressing demand for heating, ventilation, and air conditioning (HVAC) solutions that support these goals. This study emphasizes the critical need to reconsider traditional HVAC strategies in the face of green building advances. It advocates for the adoption of innovative HVAC technologies designed for eco-efficiency and enhanced comfort. These technologies should integrate seamlessly with sustainable construction, use greener refrigerants, and uphold environmental integrity, driving progress towards a sustainable and occupant-friendly built environment.

Based on three-dimensional optical proximity correction (3D OPC), recent advancements in 3D lithography have enabled the high-fidelity customization of 3D micro-optical elements. However, micron-to-millimeter scale structures represented by Fresnel lens design bring more stringent requirements for 3D OPC, which poses significant challenges to the accuracy of models and the efficiency of algorithms. Thus, a lithographic model based on optical imaging and photo-chemical reaction curve is developed in this paper, and a sub-domain division method with a statistics principle is proposed to improve the efficiency and accuracy of 3D OPC. Both the simulation and the experimental results showed the superiority of the proposed 3D OPC method in the fabrication of the Fresnel lenses. The computation memory requirements of the 3D OPC are reduced to below 1 %, and the profile error of the fabricated Fresnel lens reduced 79.98%. Applying the Fresnel lenses to an imaging system, the average peak signal to noise ratio (PSNR) of the image increased by 18.92 %, and the average contrast of the image enhanced by 36 %. We believe the proposed 3D OPC method can be extended to the fabrication of vision-correcting ophthalmological lenses.

Textile and clothing is one of the most important industrial sectors, not only due to the significant number of jobs generated, but also because it addresses one of the people’s fundamental needs (clothing). It is, however, a sector with a huge environmental impact, all over the world, and also an important social impact, especially in developing countries. Sustainability in the Textile & Clothing value chain is a known issue, concerning both environmental, and economical-social facets of sustainability. One way to improve sustainability in this sector is by measuring the environmental, economic and social impacts of value chain activities and, ultimately, computing an Environmental and Circular score for each batch of textile and clothing product, and an Economical and Social score for each involved firm, reflected in their products. The consumer will then have the opportunity and responsibility for selecting products with the least negative environmental, economical and social impact. This article presents the results of STVgoDigital project - PPS1, developed in Portugal with the aim of implementing the traceability of textile and clothing products, boosting sustainability in this sector, and preparing the ground for the implementation of the circular economy. More exactly, a blockchain-based textile and clothing value-chain traceability platform is presented, for tracing textile product lots and activities, along the value chain, classifying them with a score, which measures their environmental and social impact. The environmental, economic and social impact scores are based on a set of proposed indicators. The results are assessed through the presentation of a case study of a face towel (home textile) and a case study of a T-shirt (clothing).

In this paper, based on the symmetrical reliability theory, a research method for the safety factor of overall overturning stability of continuous beam bridge under carbon fiber reinforced polymers (CFRP) during cantilever and horizontal rotation construction is proposed. Under the premise that the given target reliability index is known, the method solves the overall overturning stability safety factor of continuous beam bridge under carbon fiber reinforced polymers during cantilever and horizontal rotation construction by using the symmetrical reliability analysis theory and considering the randomness of structural parameters. This method is used to calculate the safety factor and the design reinforcement aera of CRFP of overturning stability and the design reinforcement aera of CRFP of a continuous beam bridge, and the parameters are analyzed. The calculation results show that the randomness of parameters will have a great impact on the overall safety factor and the CRFP aera of overturning stability of cantilever and horizontal rotation construction of continuous beam bridge, and ignoring the influence of randomness of parameters will lead to overestimation of the overall safety factor and underestimation of the CRFP aera of overturning stability of cantilever and horizontal rotation construction of continuous beam bridge.

This paper presents experimental results of static and dynamic obstacle avoidance methods for a two-wheel mobile robot with independent control using a deep Q-learning (DQL) reinforcement learning algorithm. This is an alternative method that combines the Q-learning (QL) algorithm with a neural network. Neural networks in the DQL algorithm act as Q matrix table approximators for each pair (State - Action). The effectiveness of the proposed solution was verified through simulations, programming, and experimentation. This DQL algorithm is compared to the QL algorithm. First, the mobile robot communicated with the control script using the robot operating system (ROS). The mobile robot is code programmed using a Python in the ROS operating system combined with the DQL controller in Gazebo software. The mobile robot then performed experiments in a workshop with many different experimental scenarios. This DQL controller is improved in term of the computation time, convergence time, planning trajectory accuracy, and obstacles avoidance. Therefore, the DQL controller solved the path optimization problems for mobile robots better than the Q-learning controller.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

During the hand-eye calibration process of the manipulator, the Euclidean distance error of the calibration results cannot be calculated because the true values of the hand-eye conversion matrix cannot be obtained. In this paper, a new method of projection error analysis is presented. Error analysis is carried out by using a priori knowledge that the location of AR markers is fixed in the calibration process. The coordinates of the AR marker center point are reprojected to the pixel coordinate system, and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling in order to obtain the Euclidean distance between the two coordinates as the basis for error analysis. Experimental results show that the proposed optimization method can greatly improve the accuracy of hand-eye calibration results.

High-performance insulation and airtight building envelopes are the two most important factors to optimize the building energy efficiency and thermal performance. However, thermal performance of building envelopes can be significantly affected by thermal bridging. Thermal bridges can take place at different locations of the building envelope, causing added transmission losses, increasing heat flow, lower inner surface temperatures. Thermal bridges are regarded in all national regulations for new buildings of all states of European Union (EU) but there are no specifications assessing balconies to wall junctions and the impact on the building thermal performance is not well regulated. In this paper, balcony to wall thermal bridge assessment using infrared thermography is highlighted. The originality is that, infrared thermography, has been applied to a real living latest building in the north east of France showing that there is noncompliance in the national regulations as some exigencies are not met. Thermography of thermal break windows and window doors has also revealed air leakage. The goal, is to illustrate that thermal imaging is a means of quality assurance to new building specifications and should include a clause specifying quality assurance procedure for balconies junction in national building regulations for all states if the EU.

The aims of this paper can be traced back to goals number seven, twelve and thirteen introduced by Agenda 2030, namely "affordable and clean energy", “responsible consumption and production” and "climate action”, respectively. This is due to the fact that the present work supports decarbonization processes acting in a direct way on the electricity consumption achieved by plants pertaining to the waste industry; in fact, this study aims at the realization of a machine learning model for predicting the energy consumption achieved by a mechanical biological treatment (MBT) waste plant located in central Italy, given the distribution of the entering waste. This model is implemented in MATLAB. The model can be used to tune the distribution of the entering waste in order to adapt the plant energy consumption to the capability of the energy sources and can serve as a play-ground model for other energy transformation plants. The results of the study, which feed the literature on the application of artificial intelligence to real industrial plants, could be used to determine energy efficiency actions that could be incorporated into Property’s strategic planning.

This paper delves into the multifaceted domain of the aerospace industry, examining its evolution, current challenges, and imperative focus on quality management and process integration. The aerospace sector, driven by technological advancements and a burgeoning global demand for air travel and freight transport, necessitates a thorough analysis of its industrial fabric and operational intricacies. This research endeavors to analyze the dynamics of the aerospace industry, pinpoint its challenges, and propose an integrated approach to enhance efficiency, quality, and sustainability. The primary goals encompass understanding the evolving industry landscape, identifying critical challenges, and offering innovative solutions by amalgamating principles of Industry 4.0 into quality management and processes within the aerospace sector. Through an in-depth exploration of various facets, this research underscores the pivotal role of efficient processes and integrated quality management in achieving sustainable growth and competitiveness in the aerospace industry. By aligning with the paradigm of Industry 4.0, organizations can optimize their operations and contribute to the industry's advancement, delivering safer and more cost-effective aerospace products. The study adopts a multifaceted approach, incorporating extensive literature review, critical analysis of industry trends, examination of quality management frameworks, and a thorough evaluation of the integration potential of Industry 4.0 technologies. The research also involves case studies and expert insights to validate the proposed approach. The investigation reveals that by leveraging Industry 4.0 technologies and embracing an integrated approach to quality management, the aerospace industry can significantly enhance operational efficiency, product quality, and overall sustainability. The seamless integration of processes and the implementation of advanced quality frameworks pave the way for a more competitive and future-ready aerospace industry, meeting the evolving demands of a globalized world.

This work focuses on addressing the dynamic positioning and trajectory tracking problem for a 4 degree-of-freedom (DOF) unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. To tackle this problem, a disturbance observer-based control (DOBC) scheme is proposed. The scheme is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the EAOB has the unique ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC (DOBMPC) is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms a robust adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and Robot Operating System (ROS) environment. The results show that it can effectively reject unpredictable disturbances and improve the UUV’s control performance.

This paper presents a deep learning-based multi-guidance line detection approach, enabling an autonomous four-wheeled agricultural robot to navigate strip farmland. The integration of Proportional-Integral-Derivative (PID) and Fuzzy Logic Control (FLC) systems optimizes the velocity and heading angle of robot, facilitating smooth navigation along identified guidance line. Enhance cornering maneuverability with real-time kinematics (RTK)-assisted Global Navigation Satellite System (GNSS) (RTK-GNSS) positioning. Additionally, the spraying system combined with deep learning can effectively identify weeds and crop nutrient deficiencies to achieve precise spraying. The guidance system prioritizes irrigation lines for navigation, with additional guidance from crop and furrow lines. Trigonometric analysis determines the angular deviation between the identified guidance line and the vertical line of the top view image. Experiments under diverse weather conditions demonstrate the stable navigation of robot at 12.5 cm/s, achieving up to 90% accuracy in weed and nutrient deficiency identification. The spraying accuracy for weeds and deficiencies averages 87% and 73%, respectively, underscoring the system's contribution to sustainable and precision horticulture practices.

Due to the highly nonlinear, multi-stage, and strongly time-varying marine lysozyme fermentation process, it is difficult to assure the stability and prediction accuracy of the traditional single global soft sensor model on a global scale. This study innovatively proposed a soft sensor model based on an improved seagull optimization algorithm (ISOA) combined with Gaussian process regression (GPR) weighted ensemble learning. First, the sample data set is divided into multiple local sample subsets by the improved density peak clustering algorithm (ADPC). Second, the Gaussian process regression model is optimally altered with an improved seagull optimization algorithm for the purpose of establishing the corresponding sub-prediction model. Finally, the prediction model's fusion strategy is ultimately determined depending on the degree of connection between the test samples and a subset of local pieces. Simulation results show that the proposed soft sensor model, which searches GPR based on ISOA and combines various sub-models, can predict the key biochemical parameters of the marine lysozyme fermentation process better with less prediction error under the condition of fewer training data, and it can be expanded to the soft sensor models of general nonlinear systems, according to simulation results.

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.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.

Abstract: — management of the Continuous Stirred Tank Reactor (CSTR) operation has proven to be a difficult undertaking due to the complex and ever-changing nature of its behavior, which is both nonlinear and dynamic, which is further compounded by its instability. Attempts have been made to use Proportional-Integral-Derivative (PID) controllers for such processes, but the performance is often poor due to the inherent limitations of the controllers. In contrast, heuristic algorithms have been employed to tune the controllers, but these methods can only provide optimal performance for a restricted range of parameter variations. This paper presents a novel approach that involves using a conventional gain scheduling PID controller in combination with a fuzzy gain scheduling (FGS) controller method to calibrate set-point weighted PID controllers for the CSTR process. The paper outlines the design of the controllers and evaluates their performance by subjecting them to varying input signals within the given bandwidth.

This study presents an innovative approach named Optimal Adaptive Continuous Barrier Function Terminal Sliding Mode Control (OACBF-TSMC) approach with a novel switching manifold, to control mathematical model of nonlinear systems under model uncertainties and external disturbances. The proposed OACBF-TSMC approach combines the advantages of adaptive control, continuous barrier functions, and terminal sliding mode control to achieve robust stability of the mathematical model of nonlinear systems, and by using a genetic algorithm that tries to optimize the controller parameters as much as possible so that the proposed controller is the most to have efficiency and performance. The proposed method is designed to address the challenges of systems and provide superior performance in terms of stabilization accuracy, disturbance rejection, and robustness against uncertainties. However, the critical challenge facing the proposed controller is the complete elimination of the chattering phenomenon, which is the most important problem of ordinary sliding mode controllers. The simulation results show that the system trajectories in the proposed approach have converged to the origin with minimal overshoot and under-shoot in a finite time; In addition, the chattering problem in the control inputs has been well eliminated, which makes the proposed method suitable for various systems, including chaotic systems, energy sources, and distributed production systems. Furthermore, the OACBF-TSMC approach has the potential to enhance the stability and reliability of control systems, contributing to the integration of the system states.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

Due to the imbalance between the supply and demand of oxygen, the oxygen system of iron- and steel-making enterprises in China has problems with high oxygen emission and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To relieve this problem, using a large-scale iron- and steel-making mill as a case study, the research on demand forecasting and optimal scheduling of the oxygen system was carried out. The ARIMA model and the GABP model are employed to forecast oxygen demand. Based on the forecast results, an optimal scheduling model for the oxygen system was developed to conduct optimal scheduling. The case study shows that based on the oxygen demand forecast and the optimal scheduling, the oxygen emission and the pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions are obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model is better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models are 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it is found that after the scheduling, the amount of oxygen emission has decreased by 6.32%, the pipeline pressure has decreased by 0.61%, and the energy consumption of oxygen compression has decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the oxygen system is reduced by 1.38%, which saves the electricity cost of about 9.03 million RMB per year.

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained

Collaborative robots have experienced low acceptance in applications, especially in industry. 1 This fact has attracted the attention of researchers and practitioners, who point to different causes 2 for this low acceptance. One of the main reasons is the difficulty in converging on suitable methods 3 for modeling collaborative interactions between robots and their surrounding context during the 4 requirements phase. These interactions must be elicited and modeled during requirements modeling 5 to maximize value creation through collaboration and must be formally verified, taking into account 6 the risks of human-robot interaction. However, such modeling is often not present in collaborative 7 robot design, and the choice of an appropriate approach remains an open problem. In this paper, 8 this problem is addressed from a requirements engineering perspective, a goal-oriented model and 9 a service-based approach supported by an additional verification method based on Petri nets is 10 proposed. A case study based on collaborative robots used in a hospital environment is presented.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

incipient fault diagnosis is particularly important in process industrial systems, as its early detection helps to prevent major accidents. Against this background, this study proposes a combined method of Mixed Kernel Principal Components Analysis and Dynamic Canonical Correlation Analysis (MK-DCCA). The robust generalization performance of this approach is demonstrated through experimental validation on a randomly generated dataset. Furthermore, Comparative experiments were conducted on a CSTR Simulink model, comparing the MK-DCCA method with DCCA and DCVA methods, demonstrating its excellent detection performance for incipient fault in nonlinear and dynamic system. Meanwhile, fault identification experiments were conducted, validating the high accuracy of fault identification method based on contribution. The experimental findings demonstrate that the method possesses a certain industrial significance and academic relevance.

In order to create a smart mine, it is necessary to digitize the existing emulsion pumping station. Existing research does not achieve this task, this paper designs and manufactures a high-pressure, high-flow, high-water-based mining digital controlled unloading valve (abbreviated as HLHMDCUV), which achieves good results in both theoretical analysis and experimental testing. In order to obtain the accurate dynamic characteristics, this valve was analyzed by simulation using AMESim. Based on the dynamic performance analysis and the flow field analysis inside the valve body, the structure has been optimized to improve the performance of HLHMDCUV. The results show that the HLHMDCUV has a better dynamic characteristics, which were also demonstrated in the experiment. At the same time, the HLHMDCUV is easy to disassemble and maintain. This paper provides a theoretical basis for the design of such valves, which is conducive to the subsequent promotion of intelligent mine transformation.

This paper provides a new management method (MM) to optimally operate standalone direct current (DC) photovoltaic water pumping system (PVWPS) and synchronize its hydraulic performance to variable daily irradiance. The proposed approach well considers the issues from PV generator and a positive displacement pump mismatching to deserve a hydraulic network. Accordingly, Management Method uses small pumps mounted in parallel scheme to displace the hydraulic power point (HPP) in order to satisfy the mal adequacy between supply and load. Based on experimental Study; this approach showed a yielded standalone system, possibility of running pumping system using only photovoltaic supply (Without use of capacitance), and possibility to design more economical PV systems as the maximum peak power demand for each month can be satisfied using small power pumps in parallel and adjusting their switch with accordance to varying daily irradiance. Moreover, an analytical model was developed with reference to the obtained experimental results. This model based on a daily occurring irradiance as inputs in order to predict optimal performance outputs. It can be considered as a decision tool to track power point resulting from optimal electrical and hydraulic performances of the pumping system according to changes of the daily irradiance.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

The integration of Wireless Sensor Networks (WSNs) into agricultural areas has had a significant impact and has provided new, more complex, efficient, and structured solutions for enhancing crop production. This research reviews the role of Wireless Sensor Networks (WSNs) in monitoring the macro-nutrient content of plants. The review study focuses on identifying the types of sensors used to measure macro-nutrients, determining sensor placement within agricultural areas, implementing wireless technology for sensor communication, and selecting device transmission intervals and ratings. The study of NPK (Nitrogen, Phosphorus, Potassium) monitoring using sensor technology in precision agriculture is of high significance in efforts to improve agricultural productivity and efficiency. In addition to fostering technological innovations and precision farming solutions, in future this research aims to increase agricultural yields, particularly by enabling the cultivation of certain crops in locations different from their original ones.

The dependence on Unmanned Aerial Vehicles (UAVs) has dramatically increased in many sectors around the globe. UAVs are in high demand, and their technology is developing quickly due to their sophisticated ability to handle various issues. UAVs are capable of replacing labor-intensive tasks with conducive and safe regulation. Additional tools or sensors need to be added to the UAVs system to ensure the implementation of UAVs able to serve into industrial level. The paper aims to consolidate and present a thorough understanding of the various stages of image processing pipelines deployed in UAV applications, including image acquisition, preprocessing, feature extraction, object detection and tracking, and decision-making processes. Throughout this paper, several aspects were deliberate such as strengths, limitations, and performance metrics of existing approaches, this paper seeks to provide researchers, engineers, and practitioners with valuable insights into the challenges and opportunities of image processing systems for UAVs. Ultimately, the synthesis of this knowledge will contribute to enhancing the effective-ness, autonomy, and applicability of UAVs in diverse fields such as surveillance, agriculture, disaster management, and environmental monitoring.

The article is devoted to the issues of assessing the impact of changes in the delay value in the feed direction, what determines the regenerative nature of self-excitation of the cutting system, on the dynamics of metal processing on metal-cutting machines. It is this interconnected model of the cutting system that allows to identify the main dynamic effects that occur in the cutting system, including the effect of vibrations regeneration when cutting along the track. The article also has a section dedicated to the experiment on a real metalworking machine, on which vibrations of the top of the cutting wedge of the tool were measured and based on these data, a real calculation of the delay time and the magnitude of the change in the area of the cut layer was carried out, due to variations in the real feed. The conducted studies have shown that in addition to the vibrations of the cutting tool in the direction of feeding, the vibration activity of the tool in the direction of cutting plays an essential role in ensuring the regenerative effect. The delay time constant of the operator is formed, which determines the real value of the feed during cutting.

Regular regimes of oil and oil products pipelines are studied. The problem is to develop an algorithm of effective pipeline system control that ensures the fulfillment of specified boundary conditions at the system boundaries. The product is pumped in batches, the flow is characterized by a change in temperature and pressure flow parameters over time. The developed solution method uses the principle of decomposition in the sense that each computational iteration is im-plemented in 3 stages, in three nested loops. The inner cycle is connected with the material bal-ance relations, at the next cycle the flow equations are introduced and the fulfillment of the 2nd Kirchhoff's law is monitored. If the flow process is significantly affected by temperature, then an external cycle is also implemented, correcting the solution so that the energy conservation equa-tion is satisfied. In the calculation procedure, some computational difficulties may arise, which consist in choosing an appropriate optimization method. On a specific example, the possibility of using various library programs to solve the problem was checked. The developed method is suita-ble for the analysis of systems of arbitrary topology. Since the method is not time-consuming, it can be recommended for online control of oil and oil products supply systems. The paper opens a new direction in the theory of hydraulic circuits. It uses both traditional methods of continuum mechanics and stochastic models.

Recently, new advanced techniques have been proposed to improve the quality of smart grids that use intermittent renewable energy sources. One of the most effective techniques is active filtering, which utilizes a power electronic converter in the smart load. This technique has the potential to enhance the power quality of the supply voltage by minimizing current harmonics, which is crucial. In this article, we propose a new command intelligence system based on Fuzzy PI fractional (fuzzy FOPI) and GA to control the harmonic currents generated by an active filter powered by a wind system.

This work investigates the sliding mode control (SMC) problem for a class of uncertain switched systems subject to asynchronous switching and an assigned finite time constraint. Two important issues are how to ensure the reachability of state trajectories within the assigned time and the input-to-output finite-time stability (IO-FTS) of the closed-loop switched systems during the whole phase under asynchronous switching. To achieve these objectives, an asynchronous sliding mode controller with adjustable parameters is constructed to drive the state trajectories onto the sliding surface during the assigned finite-time interval. By means of the partitioning strategy, sufficient conditions for the IO-FTS of the closed-loop switched are derived during the whole phase [0,T] using the multiple Lyapunov function (MLF) approach. Additionally, the asynchronous characteristics are detailedly investigated while analyzing the reachability of a specified sliding surface. Finally, an illustrative example is given to illustrate the effectiveness of the proposed method.

The process industry is confronted with rising demands for flexibility and efficiency. One way to achieve this are modular process plants that consist of pre-manufactured modules with their own decentralized intelligence. Plants are then composed of these modules as unchangeable building blocks and can be easily re-configured for different products. Condition monitoring of such plants is necessary, but available solutions are not applicable. The authors suggest an approach in which model-based symptoms are derived from few measurements and observers that are based on manufacturer knowledge. The comparisons of redundant observers lead to residuals that are classified to obtain symptoms. These symptoms can be communicated to the plant control and are inputs to an easily adaptable diagnosis. The implementation and validation at a modular mixing plant showcases the feasibility and the potential of this approach.

Reinforcement Learning (RL) has demonstrated considerable potential in solving challenges across various domains, notably in autonomous driving. Nevertheless, implementing RL in autonomous driving comes with its own set of difficulties, such as the overestimation phenomenon, extensive learning time, and sparse reward problems. Although solutions like Hindsight Experience Replay (HER) have been proposed to alleviate these issues, the direct utilization of RL in autonomous vehicles remains constrained due to the intricate fusion of information and the possibility of system failures during the learning process. In this paper, we present a novel RL-based autonomous driving system technology that combines Obstacle Dependent Gaussian (ODG) RL, Soft Actor-Critic (SAC), and meta-learning algorithms. Our approach addresses key issues in RL, including the overestimation phenomenon and sparse reward problems, by incorporating prior knowledge derived from the ODG algorithm. We evaluated our proposed algorithm on official F1 circuits, using high-fidelity racing simulations with complex dynamics. The results demonstrate exceptional performance, with our method achieving up to 89% faster learning speed compared to existing algorithms in these environments.

Since the come up in the 70´s, various decentralized control methodologies have been developed to deal with the challenge of controlling complex and/or spatially distributed systems with multiple inputs and multiple outputs (MIMO), e.g., chemical plants, power systems, water systems, etc. In general, the use of distributed information and control structures requires to synthesize control laws in a constrained (decentralized) information structure. The article presents another version of the unified frequency domain robust decentralized controller design method [1]. The proposed design procedure is appropriate for uncertain dynamic MIMO systems given as a set of transfer function matrices. Its main framework is provided by the Equivalent Subsystems Method [1, 24] guaranteeing fulfillment of the necessary and sufficient stability condition of the overall closed-loop system if individual closed-loop equivalent subsystems are stable. Generating sets of equivalent subsystems for all transfer function matrices describing the uncertain MIMO plant allows to use the QFT method [19] to independently design local robust SISO controllers that constitute the resulting decentralized controller implemented on true subsystems. The developed design procedure is verified and illustrated on a case study on robust decentralized level controller design for a quadruple tank process [2, 24].

This paper studies the trajectory tracking control of an autonomous underwater vehicle (AUV) based on a stochastic uncertain nonlinear system. We investigate the time-varying gain adaptive control method looking for possible approaches to alleviate the heavy computational burden. Under nonlinear growth, conditions satisfy polynomial growth conditions. These two problems are resolved the fast response time and good path tracking, respectively. Novel adaptive algorithms are developed exploiting the dynamic properties of the AUV motion. By appropriately turning the original controller design problems into parameters choosing problems and then solving them in a functional time-varying observer technical theorem. In order to deal with the station error of system converges to arbitrarily small domains with stochastic uncertain disturbance. A coordinate transformation is proposed for all system states to meet boundedness. We show that the convergence of the AUV trajectory errors can be guaranteed by the proposed contraction constraints in the stability analysis, closed-loop stability is proved, and the system is asymptotically probabilistic in the global scope. They are taking advantage of the guaranteed stability. Extensive simulation studies on the AUV model demonstrate the effectiveness and robustness of the proposed approach. A real-time time-varying gain constructive control strategy is further developed for the Hardware-in-the-loop simulation; the controller design results are imported into the AUV actuator model to verify the effectiveness of the controller design.

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers the practical application of the theoretical principles of converged security presented in the publication to a functional sample, deployed and tested in practical conditions of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the context of resilience assessment is defined. In the beginning, the general principles of security/ safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment method is presented for better understanding. Subsequently, PSIM and SIEM systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

Currently, a wide variety of velocity profiles are used in motion control. Thus, this paper compares the energy consumption of three parabolic, trapezoidal, and S-curve profiles. We studied the case of using embedded systems since they are a powerful solution to monitor and coordinate the movement of devices and machinery in various industries combined with motion control applications. These integrated systems allow precise and efficient control of motors and actuators through control and feedback algorithms. In addition, we propose an alternative methodology for implementing motion controllers using an Advanced RISC Machine (ARM) microcontroller, which computes the trajectory and the control action in real-time. We conducted an experiment using a linear plant composed of a direct current (DC) motor coupled to an endless screw where a carriage is mounted that can move mechanically along a rail a distance of 1.16m. In addition, a 4096 pulses per revolution (PPR) encoder is connected to the motor to measure position and angular velocity, which compute the distance the carriage has moved in meters. A current sensor is used to assess the energy consumption, and 40 tests for each profile were carried out to compare the energy consumption for the three-motion profiles considering cases with and without load on the carriage.

In this paper, a robust nonlinear dynamic controller is designed for a wind turbine with a permanent magnet synchronous generator. The wind turbine is subject to variations in all the parameters appearing in its mathematical model. Furthermore, the wind velocity is considered unavailable for direct measurement. This situation is of particular interest in practical applications, where only the nominal parameter values are known, and accurate wind velocity measurement is challenging due to perturbations caused by the turbine itself, even with appropriate sensors. The problem addressed in this work involves tracking the reference angular velocity corresponding to the wind velocity. To achieve accurate tracking, appropriate compensation of the perturbation terms resulting from parameter uncertainties and wind estimation errors is required. To address this problem, an estimator of the wind velocity is utilized, along with high–order sliding mode parameter estimators, to ensure high–performance operation of the turbine.

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. In 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.

This paper introduces an alternative method to design the path following controller of an under-actuated spherical mobile robot, which has a single pendulum structure its inside as a driving mechanism. The mechanism has an under-actuated problem; two inputs for the roll and pitch but no direct input for the yaw. In order to overcome this problem, the proposed method defines a virtual reference input for the heading direction. The virtual reference input works as a control input since it can be arbitrarily designed just like the reference input of a conventional system. The control scheme using the defined virtual reference input achieves the stabilization and path following control of the under-actuated spherical mobile robot without any additional control technique. The stability of the proposed control scheme is guaranteed by Lyapunov stability analysis, and simulation results are given to demonstrate its performance.

ORB (Oriented FAST and Rotated Brief) features are the most commonly used features in visual SLAM and visual odometry that have high computational speed, and rotation scale invariance. However, due to the homogenization of ORB features, the corner properties of specific features are poor in some artificial environments, and it can lead to poor matching performance when the environmental texture is not rich. The underlying reason is that the pixel grayscale changes in the less-richly textured regions on the image are not obvious, leading to a certain degree of mis-matching in the matching process. At the same time, when the camera motion speed is low, there is much overlap between adjacent frames. This results in minimal or almost no changes in the projection of feature points, and the system is highly sensitive to errors, In this case, even small errors can cause significant fluctuations in the calculation results. In response to the above issues, an improved feature point method for stereo vision mileage calculation is proposed to solve the problem of system stability deterioration when cameras move at low speeds in artificial envi-ronments. Firstly, weight calculation based on different texture regions is used to solve the problem of low corner properties of feature points caused by environmental texture differences. Then, keyframes are used for motion model estimation to improve the system's stability under low-speed motion. The experimental results of the dataset showed that the key frame rate was optimal between 10% and 12%, and the positioning accuracy was higher than these open-source systems compared with three common open-source VO systems.

This paper discusses the use of networks of Inertial Measurement Units (IMU) for the reconstruction of trajectories from sensor data. Logistics is a natural application domain, to verify the quality of the handling of goods. This is a mass application and the economics of logistics impose that IMUs to be used must be low cost and use basic computational devices. The approach in the paper converts a strategy from the literature, used in the multi-target following problem, to reaching a consensus in a network of IMUs. The paper presents results on how to achieve the consensus in trajectory reconstruction, along with covariance intersection data fusion of the information obtained by all nodes in the network.

Intelligent transportation systems (ITS) are at the forefront of advancements in transportation, offering enhanced efficiency, safety, and environmental friendliness. To enable ITS, autonomous systems play a pivotal role, leveraging breakthroughs in autonomous driving, data-driven modeling, and multiagent control strategies to establish sustainable and coordinated traffic management. The integration of connected and automated vehicles has garnered significant attention as a potential solution for alleviating traffic congestion and improving fuel economy, achieved through global route optimization and cooperative driving. This study focuses on a predictive control perspective to address the cooperative cruise control problem. Online decision-making is employed during the driving process, utilizing information gathered from the network. By employing bargaining games to establish an operating agreement among vehicles, we formalize a synchronization approach based on predictive control theory. Ultimately, these findings are put to the test in an emulation environment within a hardware-in-the-loop system.

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. First, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. Simulation and physical experiments are carried out to evaluate the performance of the proposed method. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in swarm foraging.

The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on autoencoder (AE) and long short-term memory (LSTM) network is proposed to improve the predictability and effectiveness of health indicator in this study. Firstly, an unsupervised method and overall framework for HI construction is built based on deep autoencoder and LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, like mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification, etc. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and vanilla autoencoder model. Result shows that LSTM-AE model outperforms the PCA and Vanilla-AE model in metrics of monotonicity, trendability, prognosability and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of system, which is helpful for further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

In this paper, a novel strategy is adopted in a degradation model to affect the implied lifetime distribution. The multiplicative degradation model is utilized as a postulate in the model. It will be established that the implied lifetime distribution forms a classical mixture model. In this mixture model, time-to-failure is lying with some probabilities between two first passage times of the degradation process to reach two specified levels. Stochastic comparisons in the model under a change in the probabilities are studied. Several examples are provided to highlight the applicability of the results in the cases when typical degradation models are candidate.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

The development of knowledge graphs for water resources as a tool for studying sustainable regional development is a relevant task, as the increasing deterioration of water bodies affects the ecology, economy of the country, as well as the health of the population living near water bodies. Solving this problem requires the development of water resource monitoring methods based on semantic data analysis is required to detect and track sources of pollution and provide recommendations to support decision-making. This research proposes a method of semantic analysis for modeling knowledge graphs and constructing rules that enable the integration of data from heterogeneous sources, semantic compatibility, support for reasoning and inference, facilitate queries and search, and represent knowledge in the field of water resource monitoring. As a result, an ontology based on the SWRL language is created, which enables the expression of complex relationships and derivation of new knowledge, SSN for interaction between the ontology and sensor data, and Time Ontology, which models and analyzes information related to temporal aspects.After populating the ontology and establishing logical connections, SPARQL queries are implemented to retrieve new knowledge about water resources.

Soft tactile sensors based on piezoresistive materials have various large-area sensing applications. However, their accuracy is often affected by hysteresis, a phenomenon that poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fibre-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units with different layer configurations (1, 3, 6, and 12) were designed. These sensor units underwent force sequences to collect corresponding output resistances. A backpropagation network was trained using these force sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement establishes the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for force measurement and control, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Notably, the complete elimination of hysteresis in tactile sensors may not be feasible. Nevertheless, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

This paper presents a method of fusion of identification (attribute) information provided by two types of sensors: combined primary and secondary (IFF) surveillance radars and ESM (Electronic Support Measures). In the first section, the basic taxonomy of attribute identification is adopted in accordance with the standards of STANAG 1241 ed. 5 and STANAG 1241 ed. 6 (draft). These standards provide the following basic values of the attribute identifications: FRIEND, HOSTILE, NEUTRAL, UNKNOWN and additional values: ASSUMED FRIEND and SUSPECT. The basis of theoretical considerations is Dezert-Smarandache theory (DSmT) of inference. The paper presents and practically uses for combining identification information from different ESM sensors and radars six information fusion rules proposed by DSmT - the Proportional Conflict Redistribution rules (PCR1, PCR2, PCR3, PCR4, PCR5 and PCR6). In the paper, rules of determining attribute information by ESM sensor equipped with the data base of radar emitters are presented. It was proposed that each signal vector sent by the ESM sensor contained an extension specifying a randomized identification declaration (hypothesis) - basic belief assignment (BBA). The paper also presents a model for determining the basic belief assignment for a combined primary and secondary radar. Results of the PCR rules of sensor information combining for different scenarios of radio-electronic situation (deterministic and Monte Carlo) are presented in the final part of the paper. They confirm the legitimacy of the use of Dezert-Smarandache theory into information fusion for primary radars, secondary radars and ESM sensors.

The development of hybrid unmanned aerial underwater vehicles (HAUVs) compatible with the advantages of the aerial vehicles and the underwater vehicles is of great significance. This paper presents the first study on a new HAUV layout using four rotors to realize the medium crossing motion of a transverse slender body similar to the fuselage of a missile or a submarine, that is the hybrid aerial underwater quadrotor (HAUQ). Then a robust control strategy is proposed for the take-off HAUQ on the water in the presence of unknown disturbances and complex model dynamic uncertainties. As a semi-submersible HAUQ rises straightly from the water, the inside of the slender fuselage placed horizontally is filled with water. The center of the mass, the moment of inertia, and the arm of force of the HAUQ will change rapidly in the takeoff phase from the water since the rapid non-uniform change of mass caused by the passive fast drainage. It is difficult to establish a accurate mathematical model of the complex dynamic changes caused by the multi-media dynamics, the fast changing buoyancy, and the added mass crossing air–water surface. Therefore, an uncertain kinematic and dynamic model is established through the passive fast nonuniform change and the complex dynamics are considered as the unknown terms, and the external disturbances of gust and other factors are assumed as the bounded disturbance input. A robust design approach is introduced to deal with the fast time-varying mass disturbance based on the input-to-state stability (ISS) theorem. The complex dynamics are estimated using the basis function and the unknown weight parameters, and the adaptive laws are adopted for the on-line estimation of the unknown weight parameters. Consider the residual disturbance of the uncertain nonlinear system as a total disturbance term, a disturbance observer is introduced for disturbance observation. The numerical simulation shows the feasibility and robustness of the proposed algorithm.

Taking a certain type of test centrifuge as the research object, the finite element model of the test centrifuge is established, the vibration characteristics and aerodynamic performance of the test centrifuge are analyzed, and the structural optimization design of the test centrifuge is carried out. In this paper, the load is applied according to the actual working condition of a certain type of test centrifuge. The vibration of the mounting seat of the test centrifuge is analyzed and the structure of the mounting seat is optimized. After optimization, the vibration of the mounting seat is 77.38 % lower than that of the original mounting seat. Then, the aerodynamic analysis of the test centrifuge is carried out. The analysis results show that the test centrifuge moves more smoothly under the whole package shell and the steady flow shell, and the resistance decreases and the shaft load decreases. Finally, the steady flow shell of the test centrifuge is optimized. The analysis shows that the increase of the width of the steady flow shell can reduce the resistance coefficient, which is helpful to the stability of the test centrifuge during operation and reduces the unbalanced response of the system caused by air resistance.

In this paper, a multirotor UAV platform based on the PX4 ecosystem was discussed, which in combination with the MATLAB Simulink programming environment enables utilization in various aspects of mechatronic engineering education. Extensive testing of the UAV platform control module was performed, in order to enable implementation in engineering education. In the design phase of the educational platform, take-off-the-shelf components were selected and tested, and airframe parts were manufactured using rapid prototyping technologies. The proposed educational platform with four rotors was presented and tested. The learning outcomes of the undergraduate mechatronics professional study program according to the Croatian Qualifications Framework and related to the considered platform are shown. A preliminary framework of laboratory exercises for the field of mechatronic systems control is investigated.

This paper presents an analysis of the sensitivity of controlling an autonomous surface object among a group of encountered objects to the inaccuracy of state control process data. For this purpose, a safe and optimal model of the control process was defined. An algorithm for determining the optimal and safe trajectory of an object, based on the multi-object game model, was developed; for comparative analysis, an algorithm for calculating only the optimal trajectory, not taking into account the maneuverability of other objects, was additionally developed. First, simulation-based studies of the algorithms enabled a comparative analysis of a number of different acceptable strategies for optimally shifting the trajectory of maneuvering objects from a single initial direction. Thereafter, the main goal of this paper was implemented: an analysis of the sensitivity characteristics of safe control, assessed with the risk of collision, both on the inaccuracy of navigation data and on the number of possible control strategies. Finally, final conclusions and a plan for further research on the subject of the paper were formulated.

Starting from the fact that in sensorless control systems of Permanent Magnet Synchronous Motor (PMSM) the load torque can have short and significant variations, this paper presents the sensorless control of a PMSM based on a Linear Adaptive Disturbance Rejection Controller (LADRC) type controller. Essentially, the successful operation of the LADRC controller to achieve PMSM rotor speed control performance depends on a good estimation of the disturbances acting on the system. Traditionally, an Extended State Observer (ESO) is used to make such an estimate. In this paper, it is proposed to use a Disturbance Observer (DO) to estimate the external disturbances, and after their rejection, the LADRC controller ensures an equivalent global behavior of the control system with an ideal double integrator, and thus increased ease in achieving the desired control performance. Control structures and Matlab/Simulink implementation of the sensorless PMSM control system based on LADRC controller with ESO/DO type observer are presented, as well as its use in tandem with a Reinforcement Learning Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) specially trained agent that provides correction signals for more accurate estimation of external disturbances and hence improved control performance. Qualitatively superior performance is achieved by using LADRC with the RL-TD3 agent control structure in terms of parametric robustness, response time, and steady-state error. Also, by calculating the fractal dimension (DF) of the controlled signal and the PMSM rotor speed, it is found that the higher the DF, the better the performance of the control system. The validation of the superiority of the proposed control structures is carried out by means of numerical simulations in the Matlab/Simulink environment.

The paper deals with one new property of the linear time-optimal control problems with real eigenvalues of the system, no matter of the number of eigenvalue multiplicity. This property unveils a prospective of synthesizing the time-optimal control without the need of describing the switching hyper-surfaces. The already chrestomathy example of the time-optimal control of a double integrator is resolved by the new technique and compared with the classical synthesis.

The complexity of objects and control systems increases the requirements for mathematical models. The structural identifiability (SI) assessment of nonlinear systems is one of the identification problems. Until now, this problem solves by parametric methods using various approximation methods. This approach is not always effective under uncertainty. We apply an approach to SI estimation based on the analysis of virtual structures. The requirements form for the system input based on the expansion of the excitation constancy property and S-synchronizability. The approach generalization to structural identifiability used in the analysis of systems with symmetric nonlinearity is given. homothety and identifiability conditions for systems with non-symmetric nonlinearities (SNN) obtained. The detectability and recoverability proofed for virtual frameworks (VF) guaranteed the SI estimation under uncertainty. The conditions under which the non-symmetric nonlinearity is hypothetical symmetric nonlinearity obtained. SI estimation examples considered for closed nonlinear systems under uncertainty.

The paper analyzes the phenomenon that condition the durability and reliability of a type of dental bur based on the wear of the active part and with effect on its quality. For the experimental study, a conical-cylindrical dental bur and a sample dental material in cobalt-chromium alloy, cylindrical shape, tested on a specially made experimental installation were used. The paper was limited to the consideration of a single parameter (loss of mass, mw by wear of the active part of the dental bur tested) for the establishment the durability and reliability, useful for the extension of the lifetime or even optimization of its operation. The wear phenomenon of the active part of dental bur is studied based on the results and experimental data obtained in the work process that was validated by interpolation and led to polynomial functions which approximate very well the dependent parameter, mw, considered in the experimental program. The results of the interpolation showed that in the first 11 h of work, the dental bur works with high efficiency, after which it can be easily decommissioned (optimizing operation, possibly constructive solutions); i.e., it should be replaced with a new one (criterion for replacing of worn dental bur).

Due to the accelerated development of the offshore method of oil production in recent decades, especially in the Arctic region, the risks of crude oil pollution of the waters of the World Ocean are inevitably increasing. These oil spills during the exploitation of shelf deposits, transportation of oil from production sites and other anthropogenic impacts have a devastating impact on the marine biosphere, economy and life of coastal regions. For operative monitoring of water surface and fixation of oil spills it is necessary to use modern, effective and reliable methods, remote monitoring tools. One of such methods is radar. This article deals with increasing the efficiency of the radar method of water surface monitoring by means of constructive modification of the radar antenna array and changing the shape of the radio signal. As a result of the literature review, theoretical analysis of radar and conducted mathematical modeling it is substantiated the increase of radar monitoring efficiency by using phased active antenna array and construction of the cosecant-squared pattern diagram of the radio signal.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

Industrial process control systems commonly exhibit features of time-varying, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We established the LSTM-CNN-ARX model by utilizing a fusion of long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent exogenous variable autoregressive (SD-ARX) model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporating the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model's local linear and global nonlinear features. The control comparison experiments conducted on a water tank system (WTS) show the effectiveness of the developed models and MPC methods.

This article seeks to strengthen efforts in technological developments based on the classification of PET plastic that positively impacts sustainable development and contributes to suitable solutions in collection centers in Mexico. Three experiment designs and machine learning tools for data processing were developed. For the experimentation, three factors were considered: the bottle's size, the amount of liquids inside the bottle, and the label on the bottle. The first experiment was to identify the distance of the sensor from post-consumer PET bottles. The second experiment was to determine the detection ability of the sensor with different levels of liquids inside the bottles, and the third was to determine the detection ability on the bottle labels. A digital lux meter on a microcontroller was developed to monitor illuminance on post-consumer PET plastic when it contains liquid as it passes through a conveyor belt for processing at an average rate of three bottles per second. With the implemented methodology, liquids were satisfactorily detected inside the transparent PET bottles when they had beverages between 25% and 100% of their capacity. Finally, this paper highlights that it is possible to implement an affordable design to identify bottles with liquids for collection centers.

The device and method in accordance with the constant pressure regulation of micro droplet PCR in microfluidic chips are developed to explore the optimization way for the micro droplet movement, fragmentation, and bubble generation in microfluidic chips in existing digital PCR technology. In the developed device, an air source device is adopted to regulate the pressure in the chip, such that micro droplet generation and PCR amplification without bubbles can be achieved. In 3 min, the sample in 20ul will be distributed into nearly 50,000 water-in-oil droplets exhibiting a diameter of about 87μm, and the micro droplet will be subjected to a close arrangement in the chip without air bubbles. The device and chip are adopted to quantitatively detect the human gene . As indicated by the experimental results, a good linear relationship exists between detection signal and DNA concentration ranging from 101~ 105 copies /μL (R2=0.999). The micro droplet PCR devices based on constant pressure regulation chips exhibit a wide variety of advantages (e.g., achieving high pollution resistance, micro droplet fragmentation and integration avoidance, reducing human interference, and standardizing results). Thus, micro droplet PCR devices based on constant pressure regulation chips have promising applications in nucleic acid quantification.

To improve the measurement accuracy of wood density and study the linear correlation between the drill feed resistance and wood density, a new micro drill instrument that can can simultaneously measure the rotation resistance and feed resistance of the drill needle was designed. The test wood samples included hardwood, softwood and conifer. The absolute dry density of each wood sample was measured. The drill resistance data was tested by using self-developed micro drill instrument and Resistogaph 650-SC. 4 linear models between drill resistance and the absolute dry density of wood . The results showed that: the statistical indicators of each model of the self-made micro drill resistance in-strument were better than the corresponding indicators of Resistogaph 650-SC; the coefficient of determination of the linear regression model between the feed resistance of the self-made micro drill resistance instrument and the absolute dry density of wood was 0.946; the statistical indicators of model including rotation resistance and feed resistance, were better than those of the model only including rotation resistance. Therefor, the design proposed in the article is reasonable, and increasing the feed resistance can improve the measurement accuracy of the micro drill resistance instrument for measuring wood density.

Fires pose a significant threat to human life and property. An autonomous fire detection and fighting robot has the potential to mitigate the challenges associated with firefighting. This paper presents the implementation of an autonomous fire detection and fighting robot equipped with sensors, algorithms, and actuators to detect, locate, and suppress fires autonomously. The robot's mechanical and electrical components were designed, prototyped, and tested in a controlled environment to evaluate its ability to navigate through obstacles, detect fires, and suppress them autonomously. The results show that the robot effectively detects and suppresses fires, making it a promising solution for firefighting applications. The study contributes to the development of autonomous robots for firefighting, enhancing the safety of firefighters and the effectiveness of firefighting operations.

This paper investigates the dynamic properties of artificial neural networks using differential equations and explores the influence of parameters on stability and neural oscillations. By analyzing the equilibrium point of the differential equations, we identify conditions for asymptotic stability and criteria for oscillation in artificial neural networks. Furthermore, we demonstrate how adjusting synaptic weights between neurons can effectively control stability and oscillation. The proposed model offers potential insights into the malfunctioning mechanisms of biological neural networks implicated in neurological disorders like Parkinson's disease tremors and epilepsy seizures, which are characterized by abnormal oscillations.

This paper proposes a multi-terminal adaptive collaborative operation method to solve the problem of unstable internal force tracking in clamping and handling unknown objects by multi-terminal robots. In the proposed method, the internal command force changes the complex internal force control problem into an internal force tracking problem from multi-slave to master. Moreover, we develop an algorithm for multi-slave setups to estimate object stiffness and motion uncertainty in the direction of the internal command force according to Lyapunov theory. Finally, the impedance control generates a reference trajectory for the multi-slave to maintain the desired internal force and track the master’s motion. Several experiments are conducted on a self-made robot equipped. The experimental results show that the oscillation amplitude of each slave end is less than 1 mm, the directional oscillation amplitude is less than 1 degree during the tracking of the desired commanded internal force. For objects with low stiffness, the error of the commanded internal force is less than 1 N (6%) per slave. The error in tracking the commanded internal force for objects with high stiffness is less than 2 N (8%). The results prove the feasibility and effectiveness of the proposed method.

Control of autonomous robot motion in radial mass density field is presented. In that sense the robot motion is described as the function of the radial mass density parameters. The radial mass density field is between the maximal radial mass density and the minimal radial mass density. Between these two limited values one can use n points (n = 1, 2, . . . nmax) and calculate the related radial mass density for each point. The radial mass density is maximal at the minimal gravitational radius and minimal at the maximal gravitational radius. This conclusion is valid for Planck scale, but also for the scales that are less or higher of that one. Using the ratio of the Planck mass and Planck radius it is generated energy conservation constant with value κ = 0.99993392118. Further, in this theory it is possible to connect Planck’s and gravitational parameters as functions of the maximal (or minimal) radial mass density. In that sense the autonomous robot motion in radial mass density field is important for the control of the robot motion at micro and nano scales.

In the context of Kalman filters, the predicted error covariance matrix $\mathbf{P}_{k+1}$ and measurement noise covariance matrix $\mathbf{R}$ are used to represent the uncertainty of state variables and measurement noise, respectively. However, in real-world situations, these matrices may vary with time due to measurement faults. To address this issue in CubeSat attitude estimation, an adaptive extended Kalman filter has been proposed that can dynamically estimate the predicted error covariance matrix and measurement noise covariance matrix using an expectation-maximization approach. Simulation experiments have shown that this algorithm outperforms existing methods in terms of attitude estimation accuracy, particularly in sunlit and shadowed phases of the orbit, with the same filtering parameters and initial conditions.

This paper presents a cascade predictive control structure based on the field-oriented control (FOC) in the dq rotor reference frame for the synchronous reluctance machine (SynRM). The constant d-axis current control strategy was used and thus, the electromagnetic torque was directly controlled by the q-axis current. Because the model of the two axes currents from the inner loop is a coupled non-linear multivariable one, to non-interaction linear control the two currents, their decoupling was achieved through feedforward components. Following the decoupling, two independent monovariable linear systems resulted for the two currents dynamics that were controlled using model predictive control (MPC) algorithms, considering their ability to automatically handle the state bounds. The most important bounds for SynRM are the limits imposed on currents and voltages, which in the dq plane correspond to a circular limit. To avoid computational effort, linear limitations were adopted through polygonal approximations, resulting in rectangular regions in the dq plane. For the outer loop that controls the angular speed with a constrained MPC algorithm, as plant was considered the q-axis current closed loop dynamics and the torque linear equation. To evaluate the performances of the proposed cascade predictive control structure, a simulation study using MPC controllers versus PI ones was conducted.