Adaptive Iterative Learning Control for Robot Manipulators

Abstract

An improved adaptive iterative learning algorithm is applied on manipulators with repetitive time-varying disturbance. The saturation function was used when designed control law to reduce the torque chattering problem, and to force the manipulators to track time-varying reference signal fast, correctly and with assigned speed. The effectiveness of the proposed control system is demonstrated by Lyapunov analysis and simulation results: when the number of iteration increase, the tracking error will converge to zero uniformly with respect to the finite time interval, and chattering phenomenon reduced.