Design, analysis and experiment of robust adaptive repetitive angle tracking control for a one-DOF manipulator driven by pneumatic artificial muscles

There exist nonlinearities, uncertainties and time-varying characteristics in the system dynamics of manipulators driven by pneumatic artificial muscles (PAMs), which makes the accurate dynamic modeling and controller design challenging. In this paper, a robust adaptive repetitive control scheme is proposed to solve the periodic-trajectory tracking problem for a one-degree of freedom (one-DOF) manipulator driven by two PAMs. First, the system model of the one-DOF PAM-driven manipulator is analyzed and derived. Next, during output constraint design by using a barrier Lyapunov function, a sliding mode surface is reasonably constructed to compensate for the differential term of time-varying constraint parameter. Then, with the help of Lipchitz condition, signal replacement technique and reparameterization are implemented to deal with nonparametric uncertainties in the manipulator system for the subsequent uncertainty compensation by using difference learning method and robust feedback strategy. Moreover, the stability of closed-loop PAM-driven manipulator is proven theoretically by using Lyapunov synthesis. In the end, comparative experiments are provided on a self-built PAM testbed to demonstrated the effectiveness of the proposed robust adaptive repetitive control scheme.