Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

Abstract

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.

Design and implementation of an adaptive neural network observer–based backstepping sliding mode controller for robot manipulators

“…Rabiee et al (2019) introduced an adaptive sliding mode disturbance observer (ASMDO) and the convergence time could also be guaranteed, but the design process is too complex to be applied in nonlinear systems like robot manipulators. In the work of Liang et al (2022), a reinforcement learning controller is adopted in control of a micropositioner with an ASMDO added to reject the influence of environmental interference. Notwithstanding, it is challenging to implement the aforementioned methods in engineering applications due to the intricate design and they primarily remain in the simulation stage.…”