Repetitive process based higher-order iterative learning control law design

Abstract

Iterative learning control(ILC) has been developed for processes or systems that complete the same finite duration task over and over again. The exact mode of operation is that after each execution is complete the system resets to the starting location ready for the start of the next one. Each execution is known as a trial and the duration the trial length. Once each trial is complete the information generated is available for use in computing the control input for the next trial. This paper uses the repetitive process setting to develop new results on the design of higher-order ILC control laws for discrete dynamics. The results include the relation between the speed of error convergence and the number of previous trials included in the control law.