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首页> 外文期刊>IEEE Transactions on Industry Applications >Nonlinear Robust Optimal Control via Adaptive Dynamic Programming of Permanent-Magnet Linear Synchronous Motor Drive for Uncertain Two-Axis Motion Control System
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Nonlinear Robust Optimal Control via Adaptive Dynamic Programming of Permanent-Magnet Linear Synchronous Motor Drive for Uncertain Two-Axis Motion Control System

机译：不确定线性两轴运动控制系统永磁直线同步电动机驱动器自适应动态规划的非线性鲁棒最优控制

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摘要

In this article, a nonlinear robust optimal control (NROC) scheme for uncertain two-axis motion control system via adaptive dynamic programming (ADP) and neural networks (NNs) is proposed. The two-axis motion control system is an X-Y table actuated by permanent-magnet linear synchronous motor servo drives. First, the motions of the tracking contour in X-axis and Y-axis of the X-Y table are stabilized through feedback linearization control (FLC) laws. However, the control performance may be destroyed due to parameter uncertainties and compounded disturbances. Therefore, to improve the robustness of the control system, an NROC is designed to achieve this purpose. The tracking control problem of the X-Y table with uncertainties is transformed to a regulation problem. Then, it is solved by an infinite horizon optimal control using a critic NN. Consequently, the NN is developed via ADP learning algorithm to facilitate the online solution of the Hamilton-Jacobi-Bellman equation corresponding to the nominal system for approximating the optimal control law. The uniform ultimate boundedness of the closed-loop system is proved utilizing the Lyapunov approach and the tracking error asymptotically converges to a residual set. The validation of the proposed control schemes are carried out through experimental analysis. The control algorithms have been implemented using a DSP control board. A comparison of control performances using FLC, adaptive FLC, and FLC-based NROC is investigated. From the experimental results, the dynamic behaviors of the two-axis control system using the proposed FLC-based NROC can achieve robust optimal control performance against parameter uncertainties and compounded disturbances.
机译：本文提出了一种通过自适应动态规划（ADP）和神经网络（NNs）的不确定两轴运动控制系统的非线性鲁棒最优控制（NROC）方案。两轴运动控制系统是一个X-Y工作台，由永磁线性同步电动机伺服驱动器驱动。首先，通过反馈线性化控制（FLC）规律来稳定X-Y工作台在X轴和Y轴上的跟踪轮廓运动。但是，由于参数不确定性和复合干扰，可能会破坏控制性能。因此，为了提高控制系统的鲁棒性，设计了NROC来达到此目的。将具有不确定性的X-Y表的跟踪控制问题转化为调节问题。然后，通过使用注释器NN的无限层最优控制来解决。因此，通过ADP学习算法开发了NN，以方便在线求解与标称系统相对应的Hamilton-Jacobi-Bellman方程，以逼近最佳控制律。利用Lyapunov方法证明了闭环系统的一致最终有界性，并且跟踪误差渐近收敛到残差集。通过实验分析对提出的控制方案进行了验证。控制算法已使用DSP控制板实现。研究了使用FLC，自适应FLC和基于FLC的NROC的控制性能的比较。从实验结果来看，使用基于FLC的NROC的两轴控制系统的动态行为可以针对参数不确定性和复合干扰获得鲁棒的最优控制性能。

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