Adaptive learning tracking for robot manipulators with varying trial lengths

This paper proposes adaptive iterative learning control schemes for robot manipulator systems with iteration-varying lengths. To prove the asymptotical convergence of the joint position tracking error along the iteration axis, this paper develops a new composite energy function based on the newly introduced auxiliary variables for the analysis. Moreover, the traditional assumption of identical initialization condition is relaxed to be arbitrarily varying and then an initial rectifying mechanism is introduced to tackle initial shift problem of robotic systems. Illustrative simulations on a two degree-of-freedom robot manipulator are provided to verify the theoretical results. (C) 2019 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.