A P-type iterative learning controller for uncertain robotic systems with exponentially decaying error bounds

Abstract

This paper presents a P-type iterative learning control (ILC) scheme for uncertain robotic systems that perform the same tasks repetitively. The proposed ILC scheme comprises a linear feedback controller consisting of position error and exponentially weighted velocity error with respect to the number of iterations, and a feedforward learning controller updated by the exponentially weighted velocity error from previous trial. As the learning iteration proceeds, the position and velocity errors converge uniformly to zero within error bounds that decay exponentially through the sequence of iterations with arbitrarily selected convergence rate. Consequently, the proposed ILC scheme enables analysis and tuning of the exponential convergence rate in the iteration domain in contrast to other existing P-type ILC schemes. (C) 2003 Wiley Periodicals, Inc.