A new robust neural network controller designing method for nonlinear systems

H. Chen; K. Hirasawa; J. Hu; J. Murata

A new robust neural network controller designing method for nonlinear systems

H. Chen, K. Hirasawa, J. Hu, J. Murata

Research output: Contribution to conference › Paper › peer-review

Abstract

A new designing method of a robust neural network controller against system environment changes using Universal Learning Network (ULN) is considered in this paper. With the introduced method, the worst values of system parameters can be searched as well as the optimization of controller parameters through a dual learning algorithm, which includes maximization and minimization procedures. Therefore, the robust controller can be obtained by minimizing the criterion function regarding the worst values of system parameters. Simulation results demonstrate that the system performance has been improved compared with the conventional method by using the proposed method.

Original language	English
Pages	497-502
Number of pages	6
Publication status	Published - 2001 Jan 1
Externally published	Yes
Event	International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 2001 Jul 15 → 2001 Jul 19

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'01)
Country	United States
City	Washington, DC
Period	01/7/15 → 01/7/19

ASJC Scopus subject areas

Software
Artificial Intelligence

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title = "A new robust neural network controller designing method for nonlinear systems",

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AB - A new designing method of a robust neural network controller against system environment changes using Universal Learning Network (ULN) is considered in this paper. With the introduced method, the worst values of system parameters can be searched as well as the optimization of controller parameters through a dual learning algorithm, which includes maximization and minimization procedures. Therefore, the robust controller can be obtained by minimizing the criterion function regarding the worst values of system parameters. Simulation results demonstrate that the system performance has been improved compared with the conventional method by using the proposed method.

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A new robust neural network controller designing method for nonlinear systems

Abstract

Conference

ASJC Scopus subject areas

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