Adaptive neural network state predictor for nonlinear time-delay systems

Jing Na*, Xuemei Ren, Qiang Chen, Jiping Xu, Yan Gao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

A new adaptive nonlinear state predictor (ANSP) is presented for a class of nonlinear systems with input time-delay. High-order neural network (HONN) incorporating with a special identification model is first employed to identify the unknown nonlinear time-delay system. The predicted weight updating law of neural network is calculated based on the identification, which can be used to predict the future system states. With the predicted system states feedback, the resulting controller can compensate the time-delay in the overall close-loop system. Rigorous stability analyses for the identification and state predictor are all provided by means of Lyapunov stability criterion. Experiment results for a temperature control system with large time-delay are included to demonstrate the effectiveness of the proposed scheme.

Original languageEnglish
Title of host publicationProceedings of the 27th Chinese Control Conference, CCC
Pages638-643
Number of pages6
DOIs
Publication statusPublished - 2008
Event27th Chinese Control Conference, CCC - Kunming, Yunnan, China
Duration: 16 Jul 200818 Jul 2008

Publication series

NameProceedings of the 27th Chinese Control Conference, CCC

Conference

Conference27th Chinese Control Conference, CCC
Country/TerritoryChina
CityKunming, Yunnan
Period16/07/0818/07/08

Keywords

  • Adaptive identification
  • Neural network
  • Nonlinear predictor
  • Time-delay system

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Na, J., Ren, X., Chen, Q., Xu, J., & Gao, Y. (2008). Adaptive neural network state predictor for nonlinear time-delay systems. In Proceedings of the 27th Chinese Control Conference, CCC (pp. 638-643). Article 4605152 (Proceedings of the 27th Chinese Control Conference, CCC). https://doi.org/10.1109/CHICC.2008.4605152