Angle Error-Tracking Iterative Learning Control for Pneumatic Artificial Muscle System

Abstract

In this paper, a novel angle error-track adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a pneumatic artificial muscle-actuated mechanism with nonzero initial errors. Lyapunov synthesis is used to design the adaptive learning controller and analyze the stability of closed-loop PAM system. Firstly, the system modeling for the PAM-actuated mechanism is introduced as a preparation of controller design. Then, the reference error trajectory is constructed to deal with the initial position problem of iterative learning control. The parametric uncertainties in the controlled system are estimated by using difference learning method. Robust control strategy is used to deal with nonparametric uncertainties and disturbances. By making the system error follow the desired error trajectory over the whole time interval as the iteration number increases, we derive the accurate tracking from the system state to the reference trajectory during the predetermined part time interval. Simulation results show the effectiveness of the propose angle error-tracking adaptive ILC scheme.