Application of discrete learning control to a robotic manipulator

Abstract

An effective iterative learning control law is developed in discrete time domain for improving the trajectory tracking performance of robotic manipulators repeating the same task from cycle to cycle. With this method, information on the current-cycle is intentionally introduced into the learning law such that the convergence rate can be further improved. An analysis of convergence, formulated completely in discrete time, is given. Experimental results, based on implementation on a commercial robot, are also presented and discussed in the paper which demonstrated the effectiveness of the learning control method.