Iterative learning control for networked nonlinear systems using latest information

Abstract

The iterative learning control (ILC) algorithm is constructed for networked nonlinear systems with random measurement losses modeled by a stochastic sequence. The algorithm updates regularly when the corresponding measurement is available, while updates with the latest available packet from previous iterations if the corresponding one is lost. The almost sure convergence is strictly proved, and illustrative simulations verify the effectiveness of the proposed algorithm.