Iterative Learning Control for Linear Systems With Random Actuator Faults

This paper investigates fault-tolerant control for linear systems with random actuator faults. Actuator failures occur randomly. Random variables multiplied by the healthy input are used to model the actuator failures. To cope with the occurrence of actuator faults, a pre-correction mechanism is used before the healthy input is transmitted to the actuator. Then, a P-type iter-ative learning control algorithm is proposed to deal with the effect of actuator faults, where the system uncertainty is eliminated by decreasing learning gain sequences. Convergence of the proposed algorithm is established. The input errors are shown convergent to zero in the mean-square sense, and the mean-square of output errors converges to a finite limit. In the end, numerical results are presented to verify the effectiveness of the proposed algorithm.