A multilayer recurrent neural network for real-time robust pole assignment in synthesizing output feedback control systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)271-276
Journal / PublicationIFAC Proceedings Volumes (IFAC-PapersOnline)
Issue number1
Publication statusPublished - 2002
Externally publishedYes


Title15th World Congress of the International Federation of Automatic Control (IFAC World Congress 2002)
Period21 - 26 July 2002


Pole assignment is a basic design method for synthesis of feedback control systems. In this paper, a multilayer recurrent neural network is presented for robust pole assignment in synthesizing output feedback control systems. The proposed recurrent neural network is composed of three layers and is shown to be capable of synthesizing linear output feedback control systems via robust pole assignment in real time. Convergence of the neural network can be guaranteed. Moreover, with appropriate design parameters the neural network converges exponentially to an optimal solution to the robust pole assignment problem and the closed-loop control system based on the neural network is globally exponentially stable. These desired properties make it possible to apply the proposed recurrent neural network to slowly time-varying linear control systems. Simulation results are shown to demonstrate the effectiveness and advantages of the proposed neural network approach.

Research Area(s)

  • Output feedback control, Recurrent neural network, Robust pole assignment, Self-tuning control