This article proposes a new reinforcement learning (RL)-based adaptive control design for precision motion control of a two-degree-of-freedom piezoelectric XY nanopositioning system. In this design, an actor-critic structure is developed to eliminate the effects of uncertain nonlinearities and cross-coupling motion between the two working axes. Then, an adaptive parameter adjustment mechanism is designed to optimize the control performance without a priori knowledge of the unknown perturbations. The effectiveness and superiority of the proposed method are verified by performing simulation and experimental studies. The results show that the proposed RL-based adaptive control method provides a better robust performance and smaller tracking error for the nanopositioning system.

中文翻译：

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基于强化学习的压电驱动纳米定位系统的自适应控制

本文提出了一种新的基于强化学习 (RL) 的自适应控制设计，用于二自由度压电 XY 纳米定位系统的精密运动控制。在该设计中，开发了一个行动者批评结构来消除两个工作轴之间不确定的非线性和交叉耦合运动的影响。然后，设计自适应参数调整机制来优化控制性能，而无需先验了解未知扰动。通过仿真和实验研究验证了该方法的有效性和优越性。结果表明，所提出的基于强化学习的自适应控制方法为纳米定位系统提供了更好的鲁棒性能和更小的跟踪误差。