Fault-tolerant tracking control based on reinforcement learning with application to a steer-by-wire system. Issue 3 (February 2022)

• Record Type: Journal Article
• Authors: Chen, Huan; Tu, Yidong; Wang, Hai; Shi, Kaibo; He, Shuping
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 3(2022)
• Page Start: 1152
• Page End: 1171
• Publication Date: 2022-02
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2021.12.012
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: In this paper, a novel complete model-free integral reinforcement learning (CMFIRL) algorithm based fault tolerant control scheme is proposed to solve the tracking problem of steer-by-wire (SBW) system. We begin with the recognition that the reference errors can eventually converge to zero based on the command generator model. Then an augmented tracking system is constructed with a corresponding performance index which is considered as a type of actuator failure. By using the reinforcement learning (RL) technique, three novel online update strategies are respectively developed to cope with the following three cases, i.e., model-based, partially model-free, and completely model-free. Especially, the RL algorithm for the complete model-free case eliminates the constraints of requiring the known system dynamics in fault-tolerant tracking controlling. The system stability and the convergence of the CMFIRL iteration algorithm are also rigorously proved. Finally, a simulation example is given to illustrate the effectiveness of the proposed approach.