Reinforcement Learning-Based Prescribed-Time Fault-Tolerant Fuzzy Optimal Tracking Control for Stochastic Nonlinear Systems and Its Application to Robot Arm

초록

This study presents a novel reinforcement learning (RL)-based, predefined-time tracking fault-tolerant control (FTC) scheme with prescribed performance for handling unknown stochastic nonlinear systems (SNSs) operating in various environments with actuator faults. The scheme addresses multiple actuator fault types, including loss-in-effectiveness (LIE) and lock-in-place (LIP), within a unified theoretical framework in which some actuators may become partially or completely disabled. This framework learns the stochastic nonlinear dynamics and control behaviors of the system using fuzzy logic systems (FLSs) within an RL-based identifier-critic-actor (ICA) structure. By combining prescribed performance control with predefined-time control, the proposed controller achieves fault-tolerant tracking performance, guarantees that all signals are probabilistically bounded, and preserves the output within a specified range. Unlike traditional FTC methods, which depend on knowing the system model to handle LIP faults and bias, this method addresses LIP faults without requiring prior knowledge of the system and achieves different performance levels by utilizing an RL-based ICA to adjust the FLS weights. A practical example using a single-link robot arm driven by a brushed dc (BDC) motor demonstrates the effectiveness and improved performance of the developed FTC scheme.

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