Manipulator trajectory control using neural networks: from application to theory and back again
Yichuang Jin, Tony Pipe, Alan F. T. Winfield · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
In this paper we first briefly review neural networks and some previous results of their applications on manipulator trajectory control. Then we go into the main part of the paper, i.e., theoretical analysis of neuro-manipulator control systems. It consists of control structure designs, off-line/on-line learning algorithms, and system stability proof. Two control structures are presented, both having a stability guarantee. Simulation on a Puma 560 robot and an experiment on a Mentor robot are also presented to demonstrate how to use the theoretical results and to evaluate performance of the developed control structures.