Robot trajectory control using neural networks-theory and PUMA simulations

Abstract

In this paper we investigate neural network applications in trajectory control of robotic manipulators. Most research in the field remains at an empirical level. Although other authors have claimed very good simulation or even experiment results, lack of theoretical guarantee prevents application of the results in industry. In contrast, this paper presents a neural control method which has a strict theoretical basis. The whole system (manipulator and neural network) stability is guaranteed. Simulations in PUMA robot applications are also presented. >