Distributed Adaptive Learning Control for Trajectory Tracking of Networked Robot Manipulators

This paper develops a distributed adaptive iterative learning control (ILC) method for the trajectory consensus of robot manipulators. Unlike conventional adaptive ILC, the proposed method improves the learning law through an easy-implement estimate justification mechanism. Moreover, the method under consideration does not necessitate access to the angular acceleration information of neighboring robot manipulators. With this distributed adaptive ILC, the boundedness problem of conventional adaptive ILC can be handled effectively. A simulation case is given to show the perfect consensus property of the proposed method.