Adaptive Iterative Learning Control for Robot Manipulators With Time-Varying Parameters and Arbitrary Initial Errors

In this paper, a novel error-tracking adaptive iterative learning control scheme is proposed to solve trajectory-tracking problem for a class of robot manipulators with time-varying parameters and arbitrary initial errors. Firstly, desired error trajectories are constructed for implementing error tracking strategy in the robotic systems, so as to relax the requirement of zero initial errors, which is usually assumed to be met in traditional iterative learning control algorithms. Secondly, with the help of reasonable parameterization to the robotic dynamics, the adaptive iterative control law is designed by using Lyapunov approach. Projection-free combined time-domain and iteration-domain adaptive learning strategy is adopted to estimate the unknown time-invariant parametric uncertainties, and difference learning strategy is adopted to estimate unknown time-varying parametric uncertainties. As the iteration number increases, the system error follows its desired error trajectory over the whole interval. As a result, system state can perfectly track the reference signal in the predetermined part interval. In the end, several numerical simulations are presented to demonstrate the effectiveness of the designed control scheme.