Filtering-Error Constrained Angle Tracking Adaptive Learning Fuzzy Control for Pneumatic Artificial Muscle Systems Under Nonzero Initial Errors

In this paper, a filtering-error constrained adaptive iterative learning control scheme is proposed to solve the angle tracking problem for a pneumatic artificial muscle-actuated mechanism. The adaptive learning controller is designed by a novel barrier Lyapunov function, and the filtering error of pneumatic artificial muscle system is ensured to be constrained during each iteration. The initial position problem of iterative learning control is solved by utilizing time-varying boundary layer method. Fuzzy logic system is applied to approximate the unknown nonparametric uncertainties in the pneumatic artificial muscle system, whose optimal weight is estimated by using difference learning approach. The approximation error of fuzzy logic system is tackled by robust control strategy. Simulation results show the effectiveness of the propose angle tracking adaptive learning fuzzy control scheme.