Ding Runze, Ding Chenyang, Xu Yunlang, Liu Weike, Yang Xiaofeng
Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement, Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China.
Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement, Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China; State Key Laboratory of ASIC & System, School of Microelectronic, Fudan University, Shanghai, 200433, China.
ISA Trans. 2022 Oct;129(Pt A):580-591. doi: 10.1016/j.isatra.2021.12.026. Epub 2021 Dec 27.
Uncertain dynamics and unknown time-varying disturbances always exist in servo systems and deteriorate tracking accuracy significantly. To tackle the problem, this paper presents a novel adaptive robust control scheme based on neural networks and the robust integral of the sign of the error (RISE) method. In the proposed scheme, a new neural network compensator is developed, where a reference-driven neural network and an error-driven neural network are employed to compensate for uncertain system dynamics and unknown time-varying disturbances, respectively. And an RISE-based robust feedback controller is designed to suppress uncompensated dynamics. Asymptotic tracking control of the servo system with uncertain dynamics and unknown time-varying disturbances is guaranteed by using the Lyapunov theory. Comparative experiments and simulations with different reference signals and various types of external disturbances were conducted based on a linear motor-driven stage. Experimental and simulational results verify the superior tracking performance and powerful disturbance rejection ability of the proposed method.
伺服系统中总是存在不确定的动态特性和未知的时变干扰，这会显著降低跟踪精度。为了解决这个问题，本文提出了一种基于神经网络和误差符号鲁棒积分（RISE）方法的新型自适应鲁棒控制方案。在所提出的方案中，开发了一种新的神经网络补偿器，其中采用参考驱动神经网络和误差驱动神经网络分别补偿不确定的系统动态特性和未知的时变干扰。并且设计了一种基于RISE的鲁棒反馈控制器来抑制未补偿的动态特性。利用李雅普诺夫理论保证了具有不确定动态特性和未知时变干扰的伺服系统的渐近跟踪控制。基于直线电机驱动平台进行了不同参考信号和各种类型外部干扰的对比实验和仿真。实验和仿真结果验证了所提方法具有优异的跟踪性能和强大的抗干扰能力。
