Nonlinear model-predictive control based on quasi-ARX radial-basis function-neural-network

Imam Sutrisno, Mohammad Abu Jami'In, Takayuki Furuzuki, Norman Mariun, Mohd Hamiruce Marhaban

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

Abstract

A nonlinear model-predictive control (NMPC) is demonstrated for nonlinear systems using an improved fuzzy switching law. The proposed moving average filter fuzzy switching law (MAFFSL) is composed of a quasi-ARX radial basis function neural network (RBFNN) prediction model and a fuzzy switching law. An adaptive controller is designed based on a NMPC. a MAFFSL is constructed based on the system switching criterion function which is better than the (ON/OFF) switching law and a RBFNN is used to replace the neural network (NN) in the quasi-ARX black box model which is understood in terms of parameters and is not an absolute black box model, in comparison with NN. The proposed controller performance is verified through numerical simulations to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - Asia Modelling Symposium 2014: 8th Asia International Conference on Mathematical Modelling and Computer Simulation, AMS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages104-109
Number of pages6
ISBN (Print)9781479964871
DOIs
Publication statusPublished - 2014 Apr 2
Event2014 8th Asia International Conference on Mathematical Modelling and Computer Simulation - Asia Modelling Symposium, AMS 2014 - Kuala Lumpur, Malaysia
Duration: 2014 Sept 232014 Sept 25

Other

Other2014 8th Asia International Conference on Mathematical Modelling and Computer Simulation - Asia Modelling Symposium, AMS 2014
Country/TerritoryMalaysia
CityKuala Lumpur
Period14/9/2314/9/25

Keywords

  • moving average filter fuzzy switching law
  • nonlinear model-predictive control
  • quasi-ARX radial basis function neural network

ASJC Scopus subject areas

  • Modelling and Simulation

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