An iterative learning controller for a cable-driven hand rehabilitation robot

Abstract

Robots are widely used to help post-stoke patients conduct rehabilitation training for the motor function recovery. Because of the existence of repetitiveness in the rehabilitation training, a high-order iterative learning controller (ILC) is proposed for one hand rehabilitation robot in this paper. A series of tracking experiments are conducted to verify the effectiveness and superiority of the proposed controller by comparing to the PID controller, the P-type ILC, and the PD-type ILC. Experimental results show that: (1) the average tracking errors of the P-type ILC and the PD-type ILC are smaller than that of the PID controller, and the steady-state performance of the PD-type ILC is better than that of the P-type ILC; and (2) compared to the PD-type ILC, the average transient performance index of the high-order ILC is decreased by 33.9%. The mean value and variance of the tracking error are decreased by 21.1% and 14.4%, respectively.