Alignment condition-based adaptive iterative learning control for robot manipulators

In this work, a novel adaptive iterative learning control scheme is designed for robot manipulators with uncertain parameter and external perturbation under alignment condition. The analysis of convergence of proposed control law is based on composite energy function in the iteration domain containing position tracking error, velocity tracking error and parameter estimation error along both the time and iteration axis. Rigorous analysis indicate that both position tracking error and velocity tracking error converge to zero under alignment condition by compensation for the uncertain disturbances. Simulation results also confirm and verify the effectiveness of the proposed method.