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Models of adaptive control system design for nonlinear dynamic plants based on a neural network

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Abstract

The present work is dedicated to the control problem of nonlinear dynamic plants with an incomplete mathematical description. The authors consider a synthesis method of adaptive neural networks based on some analytic design principles. The adaptation law is defined via the stabilization condition of the closed-loop system by Lyapunov’s second method.

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Authors and Affiliations

  1. Scientific Research and Experimental Institute of Car Electronics and Electric Equipment (NIIAE), Moscow, Russia

    V. E. Bolnokin

  2. Blagonravov Institute of Mechanical Engineering, Moscow, Russia

    D. I. Mutin

  3. Ho Chi Minh City University of Transport, Ho Chi Minh, Vietnam

    Ngo Anh Tuan

  4. Voronezh State Technical University, Voronezh, Russia

    A. D. Povalyaev

Authors
  1. V. E. Bolnokin
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  2. D. I. Mutin
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  3. Ngo Anh Tuan
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  4. A. D. Povalyaev
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Corresponding author

Correspondence to V. E. Bolnokin.

Additional information

Original Russian Text © V.E. Bolnokin, D.I. Mutin, Ngo Anh Tuan, A.D. Povalyaev, 2014, published in Sistemy Upravleniya i Informatsionnye Tekhnologii, 2014, No. 3, pp. 120–124.

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Bolnokin, V.E., Mutin, D.I., Tuan, N.A. et al. Models of adaptive control system design for nonlinear dynamic plants based on a neural network. Autom Remote Control 76, 493–499 (2015). https://doi.org/10.1134/S0005117915030133

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  • DOI: https://doi.org/10.1134/S0005117915030133

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