Adaptive neural network control of coordinated manipulators

Abstract

In this article, adaptive neural network control of coordinated manipulators is considered in an effort to eliminate the time-consuming and error prone dynamic modeling process which is necessary for the implementation of conventional adaptive control. After a concise dynamic model in the object coordinate space is developed for the coordinated manipulators, an adaptive neural network controller is presented by combining the techniques of neural network parameterization, adaptive control, and sliding mode control. It can be shown that the motion tracking errors converge to zero asymptotically whereas the internal force tracking error remains bounded and can be made arbitrarily small. Numerical simulations are conducted to show the effectiveness of the proposed method.