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A nonlinear model predictive control strategy using multiple neural network models

Lookup NU author(s): 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.

Publication metadata

Author(s): Ahmad Z, Zhang J

Editor(s): Wang J; Yi Z; Zurada JM; Lu BL; Hujun Y

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Advances in Neural Networks - ISNN 2006

Year of Conference: 2006

Number of Volumes: 3

Pages: 943-948

Publisher: Springer


DOI: 10.1007/11760023_139

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science v. 3972

ISBN: 3540344373