Iterative Learning Control Design for Nonlinear Systems with Reference Trajectory Switching

This paper continues the development of iterative learning control for systems performing the same finite duration task repeatedly. Each execution is known as a trial, and the finite duration of each trial is known as the trial length. In many designs the reference trajectory is not changed during the generation of the trials, but there are applications where changing, or switching, this trajectory is required. Design in this case has been addressed based on a linear model for the dynamics, and there has been some results on design for examples where a nonlinear model of the dynamics has to be used. The development of this latter case based on the use of the recently developed vector Lyapunov function-based stability theory for nonlinear repetitive processes combined with feedback linearization is the subject of this paper. Moreover, the case when the reference trajectory is required to switch after a number of trials have been completed is considered and a switching rule is developed to avoid to avoid the increase in the tracking error after switching occurs.