Design Methods of Robust Feedback Controllers by Training Neural Networks

• NAKANISHI Hiroaki

  Graduate School of Engineering, Kyoto University

• INOUE Koichi

  Graduate School of Engineering, Kyoto University

Bibliographic Information

Other Title

• ニューラルネットワークの学習によるロバストな制御系の構築法
• ニューラル ネットワーク ノ ガクシュウ ニ ヨル ロバスト ナ セイギョケイ ノ コウチクホウ

Abstract

This paper proposes two efficient methods to design a robust feedback control system by use of neural networks. The first method is based on L₂ gain, and two different neural networks are used. The controller is trained to be robust as a result of competition between neural networks. The second method is based on MiniMax optimization, and is useful to treat parametric uncertainties. In both methods, robustness of the neural network can be quantified. It is very easy to combine proposed methods so that effective methods for various problems can be derived.

Journal

• Transactions of the Institute of Systems, Control and Information Engineers

  Transactions of the Institute of Systems, Control and Information Engineers 12 (10), 625-632, 1999

  THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

Keywords

• neural network
• L<SUB>2</SUB> gain
• MiniMax optimization
• nonlinear robust control

Details

• CRID: 1390001205165633920
• NII Article ID: 10004473634
• NII Book ID: AN1013280X
• DOI: 10.5687/iscie.12.625
• ISSN: 2185811X
• ISSN: 13425668
• NDL BIB ID: 4870236
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I4870236
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed