Iterative Learning Control for Time-Varying Systems Subject to Variable Pass Lengths: Application to Robot Manipulators

Abstract

In this article, the iterative learning control (ILC) problem is investigated for a class of stochastic time-varying systems with variable pass lengths. The randomness of the pass lengths is described by the recursive interval Gaussian distribution, and a modified iteration-average operator is developed to construct the novel ILC scheme for overcoming the limitation of conventional ILC algorithms that every pass must end in a fixed time of duration throughout the repetition. The proposed ILC approach works effectively to guarantee the boundedness of the tracking errors, which is demonstrated by a practical case study on a type of robot manipulator with two joints.