Iterative learning control for differential inclusion systems with random fading channels by varying average technique

Abstract

The aim of this paper is to study iterative learning control for differential inclusion systems with random fading channels between the plant and the controller. In reality, the phenomenon of fading will inevitably occur in network transmission, which will greatly affect the tracking ability of output trajectory. This study discusses the impact of fading channel on tracking performance at the input and output sides, respectively. First, a set-valued mapping in a differential inclusion system with uncertainty is converted into a single-valued mapping by means of a Steiner-type selector. Then, to offset the effect of the fading channel and improve the tracking ability, a variable local average operator is constructed. The convergence of the learning control algorithm designed by the average operator is proved. The results show that the parameters in the varying local average operator can be adjusted to trade-off between the learning rate and the fading offset rate. Finally, the theoretical results are verified by numerical simulation of the switched reluctance motors.