Iterative Learning Control for Robot Manipulators with Non-Repetitive Reference Trajectory, Iteration Varying Trial Lengths, and Asymmetric Output Constraints

In this work, we propose a novel iterative learning control (ILC) scheme for non-repetitive reference trajectories tracking problems of robot manipulators over an iteration domain with varying trial lengths, subject to asymmetric constraint requirements on joint angles. To address iteration varying trial lengths, unlike the existing approaches based on the contraction mapping analysis, a new structure of ILC laws has been presented in this work, using analysis based on composite energy functions. A novel universal barrier function is proposed to deal with joint angle constraints. We show that under the proposed novel ILC scheme, beyond a small initial time interval in each iteration, the joint angle tracking error is uniformly converging to zero over the iteration domain, and the joint velocity tracking error is asymptotically converging to zero in the sense of certain L² norm. In the end, a simulation example on a two-degree-of-freedom robot manipulator is presented to demonstrate the efficacy of the proposed scheme.