Iterative Learning Control for the Shank Part of Lower Limb Exoskeleton

Abstract

This paper briefly introduces the relative motion control problems of the wearable exoskeleton robot. By approximating the ideal human gait as a periodical signal, an iterative learning control algorithm with a D-type updating law is applied to the shank part of the exoskeleton robot to solve the tracking problem. As the non-linearity in the dynamic model of this exoskeleton robot is leading to a degradation in the efficiency of normal iterative learning methods, a feedback linearization method is introduced to improve the overall performance of the algorithm. Finally, the effects of measurement noises to the algorithm are discussed.