Neural predictive controller of nonlinear systems based on quasi-ARX neural network

Imam Sutrisno, Mohammad Abu Jami'in, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper present a neural predictive controller (NPC) based on improved quasi-ARX neural network (IQARXNN) for nonlinear dynamical systems. The IQARXNN is used as a model identifier with switching algorithm and switching stability analysis. The primary controller is designed based on a modified Elman neural network (MENN) controller using back-propagation (BP) learning algorithm with modified particle swarm optimization (MPSO) to adjust the learning rates in the BP process to improve the learning capability. The adaptive learning rates of the controller are investigated via Lyapunov stability theorem, which are respectively used to guarantee the convergences of the predictive controller. Performance of the proposed MENN controller with MPSO is verified by simulation results to show the effectiveness of the proposed method both on stability and accuracy.

Original languageEnglish
Title of host publicationICAC 12 - Proceedings of the 18th International Conference on Automation and Computing
Subtitle of host publicationIntegration of Design and Engineering
Pages78-83
Number of pages6
Publication statusPublished - 2012 Nov 26
Event18th International Conference on Automation and Computing, ICAC 2012 - Loughborough, Leicestershire, United Kingdom
Duration: 2012 Sep 72012 Sep 8

Publication series

NameICAC 12 - Proceedings of the 18th International Conference on Automation and Computing: Integration of Design and Engineering

Conference

Conference18th International Conference on Automation and Computing, ICAC 2012
CountryUnited Kingdom
CityLoughborough, Leicestershire
Period12/9/712/9/8

Keywords

  • improved quasi-ARX neural network (IQARXNN)
  • modified Elman neural network (MENN)
  • modified particle swarm optimization (MPSO)
  • neural predictive controller (NPC)
  • stability and accuracy

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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