A novel iterative learning path-tracking control for nonholonomic mobile robots against initial shifts

In this article, we propose a novel discrete-time iterative learning control framework for robust path-tracking problem of nonholonomic mobile robots. The contributions of this iterative learning control framework are threefolds: (1) With the application of a conventional feedback-aided P-type learning algorithm, the tracking error caused by a nonzero initial shift is detected. (2) By the introduction of an initial rectifying term, a novel iterative learning control scheme is proposed to improve the tracking performance. Sufficient conditions of convergence of this approach are given. (3) The convergence of the proposed algorithm for achieving the desired trajectory over a specified interval is proven theoretically. Simulation results validate the effectiveness of the proposed scheme.