Convergence Analysis of Iterative Learning Control Systems Over Networks With Successive Input Data Compensation in Iteration Domain

This paper analyzes the convergence of iterative learning control for a class of discrete-time systems over networks with successive input data compensation. Specifically, the successively dropped input data in current iterations are compensated by the one actuator received correctly with the same time instant label in the latest iteration. Through analyzing the variation of elements in the transition matrices of the input errors at the controller side, the convergence of output errors is addressed. The analysis shows the selection range of learning gain is determined by the maximum number of input data dropped successively. Moreover, the convergence of system with successive input data compensation is guaranteed by trading the convergence speed, and the more input data are successively compensated in iteration domain, the more convergence speed of the system is reduced. Finally, numerical experiments are given to corroborate the theoretical analysis.