Neural network learning control for position and force tracking of multi-manipulator systems

In this article, an approach to improving the: performance of multi-manipul ator systems using neural network is presented. This approach is formulated in the constrained motion framework, within which a nominal feedback contr ol augmented by a neural network is derived It is shown that the closed-loo p system with the neural network learning on-line is stable in the sense th at all signals in the systems are bounded. It is further proved that the pe rformance 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 v erify the analytical conclusions are presented. The results of this work su ggest that neural networks could be used as "add-on" control modules to imp rove the performance of industrial robots in execution of tasks involving t wo or more cooperative manipulators.