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.

Finite frequency range iterative learning fault-tolerant control for discrete time-delay uncertain systems with actuator faults.

The subject area considered is discrete linear time delay systems operating repetitively on a finite time interval with actuator faults, where the system resets at the end of each operation. Regulation of the dynamics is by iterative learning control and performance goals imposed over finite frequency intervals for the case of uncertainty in the dynamic model. To derive the results, the generalized Kalman-Yakubovich-Popov lemma is used. A simulation based case study is also given to demonstrate the applicability of the new results.