Neural Network Learning Control for Position and Force Tracking of Multi-Manipulator Systems

Abstract

ABSTRACTIn this article, an approach to improving the performance of multi-manipulator systems using neural networks is presented. This approach is formulated in the constrained motion framework, within which a nominal feedback control augmented by a neural network is derived. It is shown that the closed-loop system with the neural network learning on-line is stable in the sense that all signals in the systems are bounded. It is further proved that the performance of the multi-manipulator system is improved in the sense that the “size” of a certain error measure decreases as the learning process of the neural network is iterated. Results of computer simulations conducted to verify the analytical conclusions are presented. The results of this work suggest that neural networks could be used as “add-on” control modules to improve the performance of industrial robots in execution of tasks involving two or more cooperative manipulators.