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

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. • A new fault tolerant ILC scheme is proposed for systems with actuator faults. • Actuator faults varying with both time and trial axes are considered. • Q-learning algorithm is introduced to estimate the actuator faults. • The FE results are provided for controller reconfiguration. [ABSTRACT FROM AUTHOR]