Manipulator trajectory control using neural networks: from application to theory and back again

Yichuang Jin; A. G. Pipe; A. Winfield

doi:10.1117/12.205118

6 April 1995 Manipulator trajectory control using neural networks: from application to theory and back again

Yichuang Jin, A. G. Pipe, A. Winfield

Proceedings Volume 2492, Applications and Science of Artificial Neural Networks; (1995) https://doi.org/10.1117/12.205118
Event: SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, 1995, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Yichuang Jin, A. G. Pipe, and A. Winfield "Manipulator trajectory control using neural networks: from application to theory and back again", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); https://doi.org/10.1117/12.205118

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