Angle Tracking Robust Learning Control for Pneumatic Artificial Muscle Systems

Abstract

Pneumatic artificial muscle systems have been widely used in the applications of biomimetic robots and medical auxiliary devices. The existence of high nonlinearities, uncertainties and time-varying characteristics in pneumatic artificial muscle systems brings much challenge for accurate system modeling and controller design. In this paper, a robust adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a kind of pneumatic artificial muscle-actuated mechanism. After deriving the system model according to the feature of mechanism, Lyapunov synthesis method is used to design the control law and adaptive learning laws. Robust strategy and full saturation learning strategy are jointly used to compensate parametric/nonparametric uncertainties and reject external disturbances. Alignment condition is applied to solve the initial position problem of iterative learning control. As the iteration number increases, the system state can accurately track the reference trajectory over the whole interval. In the end, a simulation example is presented to demonstrate the effectiveness of the designed control scheme.