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A nonlinear model predictive control strategy using multiple neural network models

Lookup NU author(s): Zainal Ahmad, Dr Jie ZhangORCiD


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Combining multiple neural networks appears to be a very promising approach for improving neural network generalization since it is very difficult, if not impossible, to develop a perfect single neural network. Therefore in this paper, a nonlinear model predictive control (NMPC) strategy using multiple neural networks is proposed. Instead of using a single neural network as a model, multiple neural networks are developed and combined to model the nonlinear process and then used in NMPC. The proposed technique is applied to water level control in a conic water tank. Application results demonstrate that the proposed technique can significantly improve both setpoint tracking and disturbance rejection performance. © Springer-Verlag Berlin Heidelberg 2006.

Publication metadata

Author(s): Ahmad Z, Zhang J

Editor(s): Wang, J; Yi, Z; Zurada, JM; Lu, B-L; Hujun, Y

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Neural Networks (ISNN): Third International Symposium on Neural Networks

Year of Conference: 2006

Pages: 943-948

ISSN: 0302-9743 (Print) 1611-3349 (Online)

Publisher: Springer Berlin / Heidelberg


DOI: 10.1007/11760023_139

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783540344377