Sliding mode iterative learning intermittent control for robot manipulators under nonidentical trial lengths and alignment condition

Abstract

This paper is dedicated to the iterative learning control of robot manipulators under nonidentical trial lengths and the alignment condition with intermittent input. A sliding mode iterative learning control scheme is proposed to improve tracking performance against non-repetitive disturbances and the intermittent protocol is constructed by the norm of the sliding mode function. A modified reference trajectory is utilized to accommodate the alignment condition under nonidentical trial lengths. The composite energy function is built for the convergence analysis and a simulation example with comparison is provided to illustrate the effectiveness of the proposed sliding mode iterative learning intermittent control method.