Iterative learning control for robot manipulators using the finite dimensional input subspace

Abstract

Proposes a new type of iterative learning control algorithm for manipulators, which seeks the desired input in an appropriate finite dimensional input subspace. The convergence condition of the learning law is derived, and several advantages of the proposed method are pointed out. The effectiveness of the proposed method is demonstrated by experiments.