An integrated scheme of neural network and optimal predictive control

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An integrated scheme of neural network and optimal predictive control

Li Peng, Wen Li, Guohuan Lou, Xuyan Tu

Volume 9 Issue 4

Aug. 2002

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文章导航 > 矿物冶金与材料学报（英文版） > 2002 > 9(4) : 302-304.

Cite this article as:

Li Peng, Wen Li, Guohuan Lou, and Xuyan Tu, An integrated scheme of neural network and optimal predictive control, J. Univ. Sci. Technol. Beijing , 9(2002), No. 4, pp.302-304.

Li Peng, Wen Li, Guohuan Lou, and Xuyan Tu, An integrated scheme of neural network and optimal predictive control, J. Univ. Sci. Technol. Beijing , 9(2002), No. 4, pp.302-304.

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Information

An integrated scheme of neural network and optimal predictive control

Li Peng^1,2), ,,
Wen Li²⁾,
Guohuan Lou²⁾,
Xuyan Tu¹⁾

Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China
Department of Automation, Hebei Institute of Technology, Tangshan 063009, China

基金项目:

This work was financially supported by the National Natural Science Foundation of China (No. 60075012).

通信作者:
Li Peng E-mail: pcnglil967@sina.com

计量
- 文章访问数: 236
- HTML全文浏览量: 67
- PDF下载量: 8

中文摘要

An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.

关键词:

Information

An integrated scheme of neural network and optimal predictive control

Li Peng^1,2), ,,
Wen Li²⁾,
Guohuan Lou²⁾,
Xuyan Tu¹⁾

Author Affilications

Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China
Department of Automation, Hebei Institute of Technology, Tangshan 063009, China

Funds:
This work was financially supported by the National Natural Science Foundation of China (No. 60075012).
Received: 11 November 2001;

Abstract:

An approach of adaptive predictive control with a new structure and a fast algorithm of neural network (NN) is proposed. NN modeling and optimal predictive control are combined to achieve both accuracy and good control performance. The output of nonlinear network model is adopted as a measured disturbance that is therefore weakened in predictive feed-forward control. Simulation and practical application show the effectiveness of control by the proposed approach.

Keywords:

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