A new robust neural network controller designing method for nonlinear systems

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

Research output: Contribution to conferencePaperpeer-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 languageEnglish
Pages497-502
Number of pages6
Publication statusPublished - Jan 1 2001
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: Jul 15 2001Jul 19 2001

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period7/15/017/19/01

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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