Robust Iterative Learning Control for Robot Manipulators with Input Deadzone

This work studies trajectory tracking problem for robot manipulators with actuator deadzone under arbitrary initial states. A robust iterative learning control scheme is developed according to Lyapunov theory. First, the rectification reference trajectory is constructed for handling initial position of robotic ILC systems. Then, based on system parameterization technique, a robust adaptive iterative learning control law is derived to dealing with the deadzone nonlinearity and uncertainties. The convergence performance of the system and the boundedness of the closed-loop signal are proved by theoretical analysis. At the end, a numerical simulation is carried out to demonstrate the effectiveness of the proposed robust learning control scheme.