Adaptive neural network state predictor and tracking control for nonlinear time-delay systems

Jing Na*, Xuemei Ren, Yan Gao, Griñó Robert, C. C. Ramon

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

22 Citations (Scopus)

Abstract

A new adaptive nonlinear state predictor (ANSP) is presented for a class of unknown nonlinear systems with input time-delay. A dynamical identification with neural network (NN) is constructed to obtain NN weights and their derivatives. The future NN weights are deduced for the nonlinear state predictor design without iterative calculations. The time-delay and unknown nonlinearity are compensated by a feedback control using the predicted states. Rigorous stability analysis for the identification, predictor and feedback control are provided by means of Lyapunov criterion. Simulations and practical experiments of a temperature control system are included to verify the effectiveness of the proposed scheme.

Original languageEnglish
Pages (from-to)627-639
Number of pages13
JournalInternational Journal of Innovative Computing, Information and Control
Volume6
Issue number2
Publication statusPublished - 1 Feb 2010

Keywords

  • Feedback control
  • Neural network
  • State predictor
  • Time-delay system

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