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Proceedings Paper

Intelligent controller using neural network
Author(s): Jin Wang; Fuli Wang; Jinliang Zhang; Jin Zhang
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Paper Abstract

This paper presents an intelligent PID controller based on a gradient descent learning algorithm. A BP neural network is needed to learn the characteristics of the dynamic systems. The possibility of using neural network models directly within a model-based predictive control strategy is also considered by making use of an on-line optimization routine to determine the future inputs that will minimize the deviation between the desired and predicted outputs. The controller's structure and the learning algorithm are very simple and easily realized. It can also replace the traditional PID controller, control the complex systems, and require neither process model nor more tuning parameters.

Paper Details

Date Published: 28 August 1995
PDF: 7 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217462
Show Author Affiliations
Jin Wang, Northeastern Univ. (China)
Fuli Wang, Northeastern Univ. (China)
Jinliang Zhang, Northeastern Univ. (China)
Jin Zhang, Northeastern Univ. (China)


Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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