Nanotechnology is a promising technology and has been widely applied for sustainable smart cities. As the fundamental devices for nanotechnology, piezoelectric actuators (PEAs) have gained wide attention in precision manufacturing because of the advantages of rapid response, large mechanical force and high resolution. However, the inherent nonlinearities of PEAs hinder wide applications for nano-positioning and high-precision manipulation. To eliminate these nonlinearities, various control methods have been proposed, while the optimal control of PEAs is considered rarely. Inspired by the reinforcement learning, adaptive dynamic programming (ADP) is proposed to solve the optimal tracking control problem of PEAs. In this paper, a controller based on reinforcement learning and inverse compensation is designed for the tracking control of PEAs. The experiments on the PEA platform are designed to verify the effectiveness of the proposed method. Comparisons with some representative controllers have demonstrated that the proposed controller has a better control performance.

纳米技术是一种有前途的技术，已广泛应用于可持续智慧城市。作为纳米技术的基本设备，压电致动器（PEA）由于具有响应速度快，机械力大和分辨率高的优点而在精密制造领域引起了广泛关注。然而，PEA固有的非线性阻碍了纳米定位和高精度操纵的广泛应用。为了消除这些非线性，提出了各种控制方法，而很少考虑对PEA进行最佳控制。在强化学习的启发下，提出了自适应动态规划（ADP）技术来解决PEA的最优跟踪控制问题。本文设计了一种基于强化学习和逆补偿的PEA跟踪控制控制器。在PEA平台上进行的实验旨在验证所提方法的有效性。与一些代表性控制器的比较表明，所提出的控制器具有更好的控制性能。