Iterative learning control for discrete-time switched systems with attenuation factor

Abstract

In order to decrease the effects of measurement noise on the trajectory tracking control of discrete-time switched systems, this paper proposes a discrete iterative learning control algorithm with an attenuation factor. The proposed algorithm adds a learning gain attenuated along the iteration horizon into measurement errors interfered by measurement noise for modifying the control rules of switched systems, in order to decay measurement noise as iterations increase. The convergence of each subsystem is proven rigidly with λ-norm theory, and the convergent condition of switched systems is provided. Theoretical results indicate that the proposed algorithm can effectively suppress non-repetitive measurement noise, and realize the complete tracking of the desired trajectory for the output of a discrete-time switched system within limited time. The final simulation results verify the validity of the proposed algorithm.