A Repetitive Learning Method Based on Sliding Mode for Robot Control With Actuator Saturation

Abstract

This paper proposes a sliding mode based repetitive learning control method for high-precision tracking of robot manipulators with actuator saturation. Advantages of the proposed control include the absence of model parameter in the control law formulation and the ability to remove the possibility of actuator failure due to excessive torque input levels. Lyapunov's direct method is employed to prove semiglobal asymptotic tracking. Simulation results on a three degree-of-freedom (3DOF) robot illustrate the effectiveness and improved performance of the proposed scheme.