Liang, S.; Xi, R.; Xiao, X.; Yang, Z. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners. Micromachines 2022, 13, 458, doi:10.3390/mi13030458.
Liang, S.; Xi, R.; Xiao, X.; Yang, Z. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners. Micromachines 2022, 13, 458, doi:10.3390/mi13030458.
Liang, S.; Xi, R.; Xiao, X.; Yang, Z. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners. Micromachines 2022, 13, 458, doi:10.3390/mi13030458.
Liang, S.; Xi, R.; Xiao, X.; Yang, Z. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners. Micromachines 2022, 13, 458, doi:10.3390/mi13030458.
Abstract
The robust control of high precision electromechanical systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improves the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The sterling performance is revealed with intensive tracking simulation experiments and demonstrates the improvement in the accuracy and response time of the controller.
Keywords
micropositioners; reinforcement learning; disturbance observer; deep deterministic policy gradient
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The commenter has declared there is no conflict of interests.
Comment:
This work is one of my master period‘s projects. As the application of using reinforcement learning to control mirco-positioning system is rare, we proposed a improved deep deterministic policy gradient algorithm for the trajectory tracking control of micropositioners.
Commenter:
The commenter has declared there is no conflict of interests.