Liang Shiyun, Xi Ruidong, Xiao Xiao, Yang Zhixin
State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering, University of Macau, Macau 999078, China.
Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China.
Micromachines (Basel). 2022 Mar 17;13(3):458. doi: 10.3390/mi13030458.
The motion control of high-precision electromechanitcal 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 improve 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 micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1 μm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.
诸如微定位器等高精度机电系统的运动控制，在固有高度非线性、对外部干扰的敏感性以及模型参数精确辨识的复杂性方面都具有挑战性。为应对这些问题，本文研究了一种基于干扰观测器的深度强化学习控制策略，以实现高鲁棒性和精确跟踪性能。强化学习作为一种最优控制方案已展现出巨大潜力，然而，其在微定位系统中的应用仍然很少。因此，本文将深度确定性策略梯度（DDPG）与积分微分补偿器（ID）相结合，不仅能够减小状态误差，还能提高瞬态响应速度。此外，还提出了一种自适应滑模干扰观测器（ASMDO），以进一步消除集总干扰引起的综合影响。在仿真和实际实验中，由所提算法控制的微定位器能够精确跟踪目标路径，误差小于1μm，这表明了该控制器出色的性能和精度提升。
