Reinforcement Learning-Aided Performance-Driven Fault-Tolerant Control of Feedback Control Systems
Changsheng Hua, Linlin Li, Steven X. Ding
Abstract
This article is concerned with a fault-tolerant control (FTC) scheme for feedback control systems with multiplicative faults by optimizing system performance with the aid of a reinforcement learning (RL) approach. To be specific, initially, based on the Youla–Kučera (YK) and dual YK parameterizations, a new performance-driven FTC method is proposed and its capability in dealing with multiplicative faults is proven. Then, data-driven implementation of this method using RL is elaborated. This implementation shows that RL can be applied efficiently by utilizing both plant model and data to recover the fault-induced system performance degradation. Finally, a benchmark study on an inverted pendulum system demonstrates the application of the proposed performance-driven FTC method.