Decentralized iterative learning control for switched large-scale systems

This paper is concerned with the iterative learning control problem for switched large-scale systems. According to the characteristics of the systems, a decentralized D-type iterative learning control law is proposed for such switched large-scale systems. The proposed controller of each subsystem relies only on local output variables, without any information exchanges with other subsystems. By using the contraction mapping method, it is shown that the algorithm can guarantee that the output of each subsystem converges to the desired trajectory over the whole time interval along the iteration axis. Finally, three numerical examples are given to illustrate the effectiveness of the proposed algorithm.