A multilayer recurrent neural network for on-line synthesis of minimum-norm linear feedback control systems via pole assignment

Jun Wang, Wu Guang

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

A multilayer recurrent neural network is proposed for on-line synthesis of minimum-norm linear feedback control systems through pole assignment. The proposed neural network approach uses a four-layer recurrent neural network for the on-line computation of feedback gain matrices with the minimum Frobenius norm and desired closed-loop poles. The proposed recurrent neural network is shown to be capable of synthesizing minimum-norm linear feedback control systems in real time. The operating characteristics of the recurrent neural network and feedback control systems are demonstrated by use of an illustrative example.

Original languageEnglish
Pages (from-to)435-442
Number of pages8
JournalAutomatica
Volume32
Issue number3
DOIs
Publication statusPublished - Mar 1996
Externally publishedYes

Keywords

  • Gain-scheduled control
  • Neural networks
  • Optimization devices
  • Pole assignment
  • Self-tuning control
  • State feedback

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