Robust Adaptive Iterative Learning Control for Trajectory Tracking of Uncertain Robotic Systems

Abstract

To track the trajectory of a robotic system in presence of random disturbances and modeling uncertainties, a robust adaptive iterative learning control algorithm that consists of an easy-to-design PD controller, a unique learning feedforward controller and a robust term is proposed in this paper. This new hybrid control algorithm is characterized by an easy-to design PD controller to guarantee the stability of the system status; a feedforward learning controller to calculate the desired actuator torque at each iterative step by a learning rule, and a robust control term to ensure the robustness of the system under external random disturbances. The convergence of the system is proved based on the Lyapunov stability theory. It is demonstrated by simulation results that proposed algorithm not only improves the better tracking performance, but also has obvious advantages over other control methods in terms of accelerating convergence speed.