Distributed adaptive iterative learning control for multiple robot manipulators

Abstract

A hybrid adaptive and iterative learning method was proposed to obtain distributed control protocols for multiple manipulator systems with undirected interaction topology to achieve consensus tracking of the specified desired reference trajectory. By introducing an appropriate adaptive iterative learning parameter,the proposed adaptive iterative learning control( AILC) protocol can overcome the effects of disturbances and model uncertainties of manipulators,where the AILC law of each manipulator needs only the relative information between it and its nearest neighbors. Moreover,it is shown that all manipulators can be rendered to achieve the perfect tracking of the desired reference trajectory though its information can be accessed by only a portion of manipulators,where the boundedness of both the tracking error and the control input can be simultaneously guaranteed. In addition,the Lyapunov analysis method is employed to validate the obtained results,and the effectiveness of the proposed AILC protocol is illustrated through an example.