A Simple Repetitive Learning Control for Asymptotic Tracking of Robot Manipulators with Actuator Saturation

In this paper, we provide a simple decentralized saturated repetitive learning controller for asymptotic tracking of robot manipulators under actuator saturation. The proposed control consists of a saturated nonlinear proportional plus derivative action and a saturated learning-based feedforward compensation term. A Lyapunov-like stability argument is employed to show semiglobal asymptotic tracking. Advantages of the proposed controller include an absence of modeling parameter in the control law formulation and an ability to ensure actuator constraints are not breached. This is accomplished by selecting control gains a priori, removing the possibility of actuator failure due to excessive torque input levels. The effectiveness of the proposed approach is illustrated via simulations.