Convergence and Robustness of Discrete Time Nonlinear Systems with Iterative Learning Control

Abstract

In this paper, iterative learning control design of a class of discrete time nonlinear dynamic systems with disturbances are considered. An iterative learning control law is proposed to overcome the uncertainties in system parameters and disturbances. It is shown that the system outputs, states and control inputs can be guaranteed to converge to desired trajectories in the absence of state, output disturbances and repeatability uncertainty. In the presence of these disturbances and initial state uncertainty, the tracking errors will be bounded. Experiment is carried out to verify the theory and results are presented.