Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems

Rui Wang; Zhihe Zhuang; Hongfeng Tao; Wojciech Paszke; Vladimir Stojanovic

doi:10.1016/j.isatra.2023.07.043

Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems

ISA Trans. 2023 Nov:142:123-135. doi: 10.1016/j.isatra.2023.07.043. Epub 2023 Aug 1.

Authors

Rui Wang¹, Zhihe Zhuang¹, Hongfeng Tao², Wojciech Paszke³, Vladimir Stojanovic⁴

Affiliations

¹ Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.
² Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China. Electronic address: taohongfeng@hotmail.com.
³ Institute of Automation, Electronic and Electrical Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
⁴ Faculty of Mechanical and Civil Engineering, Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, Kraljevo 36000, Serbia. Electronic address: vladostojanovic@mts.rs.

PMID: 37573187
DOI: 10.1016/j.isatra.2023.07.043

Abstract

This paper proposes a Q-learning based fault estimation (FE) and fault tolerant control (FTC) scheme under iterative learning control (ILC) framework. Due to the repetitive demands on control actuators for repetitive tasks, ILC is sensitive to actuator faults. Moreover, unknown faults varying with both time and trial axes pose a challenge to the control performance of ILC. This paper introduces Q-learning algorithm for FE to continuously adjust the estimator and adapt the changing faults. Then, FTC is designed by adopting the norm-optimal iterative learning control (NOILC) framework, where the controller is adjusted based on the FE results from Q-learning to counteract the influence of faults. Finally, the simulation on the plant of a mobile robot verifies the effectiveness of the proposed algorithm.

Keywords: Fault estimation; Fault tolerant control; Iterative learning control; MIMO systems; Q-learning.