Intelligent control of robotic manipulators: experimental study using neural networks

In recent years there has been increasing interest in universal model-free controllers. These controllers using neural networks learn about the system s they are controlling online, and thus automatically improve their perform ance. There has been a good deal of research on the use of neural networks for control, although most of the articles have been ad hoc discussions lac king theoretical proofs and repeatable design algorithms. In this paper exp erimental results on the control of robotic manipulator using neural networ ks have been provided and it has been demonstrated that neural networks do indeed fulfill the promise of providing model-free learning controllers for robotic systems and provide an excellent alternative for the control of ro botic manipulators. (C) 1999 Elsevier Science Ltd. All rights reserved.