ITERATIVE LEARNING CONTROL FOR LOWER LIMB EXOSKELETON ROBOT

Abstract

This thesis mainly focuses on the motion control problems of the wearable lower limb exoskeleton robot. Firstly, by approximating the ideal human gait as a cyclic signal, the motion control problem of the exoskeleton robot is converted to a tracking problem in a series of fixed and finite time intervals, where each interval represents a gait cycle. Then, a novel iterative learning control algorithm has been proposed. The proposed algorithm combines the concept of feedback linearization with the iterative learning control method and has advantages in response speed, tracking accuracy, and error convergence rate. Relative simulation results are given to demonstrate the effectiveness of the proposed algorithm. Finally, the adaptive oscillator method which is also feasible for the problem has been introduced and a comparison between the two methods is given. Both advantages and disadvantages of the proposed iterative learning control algorithm are discussed.

Date of Award	1 Aug 2022
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Zhirun Hu (Supervisor) & Zhengtao Ding (Supervisor)