Iterative Learning Reliable Control for A Kind of Discrete-time Nonlinear Systems with Stochastic Transmission Attenuation and Offset Fault in Actuator

This paper focuses on the reliability of the iterative learning control strategy for a kind of repeatable discrete-time models subject to transmission attenuation and offset fault produced in actuator. The attenuation is a random multiplier with respect to both time and iteration index and the fault is an additive stochastic disturbance. So, the real control input is modelled by multiplying a stochastic variable with the original control signal and adding a random bounded-disturbance function. By resorting to the time-weighted norm technique, the tracking performance is analyzed in the statistical sense and the sufficiency of convergence is established. To illustrate the effectiveness and reliability of the proposed results, numerical experiments are carried out.