Neural network-based robust integral error sign control for servo motor systems with enhanced disturbance rejection performance

Runze Ding; Chenyang Ding; Yunlang Xu; Weike Liu; Xiaofeng Yang

doi:10.1016/j.isatra.2021.12.026

Neural network-based robust integral error sign control for servo motor systems with enhanced disturbance rejection performance

ISA Trans. 2022 Oct;129(Pt A):580-591. doi: 10.1016/j.isatra.2021.12.026. Epub 2021 Dec 27.

Authors

Runze Ding¹, Chenyang Ding², Yunlang Xu³, Weike Liu⁴, Xiaofeng Yang⁵

Affiliations

¹ Shanghai Engineering Research Center of Ultra-Precision Motion Control and Measurement, Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China. Electronic address: rzding18@fudan.edu.cn.
² 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. Electronic address: dingcy@fudan.edu.cn.
³ State Key Laboratory of ASIC & System, School of Microelectronic, Fudan University, Shanghai, 200433, China. Electronic address: xuyunlang@fudan.edu.cn.
⁴ State Key Laboratory of ASIC & System, School of Microelectronic, Fudan University, Shanghai, 200433, China. Electronic address: wkliu18@fudan.edu.cn.
⁵ 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. Electronic address: xf_yang@fudan.edu.cn.

PMID: 35016800
DOI: 10.1016/j.isatra.2021.12.026

Abstract

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.

Keywords: Adaptive control; Neural network control; Robust control; Servo system.