Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Jonas B. Waller, Takayuki Furuzuki, Kotaro Hirasawa

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

7 Citations (Scopus)

Abstract

This paper applies a quasi-ARMAX modeling technique, recently presented in the literature, to a process control framework. The use of this quasi-ARMAX modeling technique in nonlinear model predictive control (NMPC) formulations applied to simple nonlinear process control examples is investigated. The quasi-ARMAX predictor can be interpreted as a neuro-fuzzy predictor, and this neuro-fuzzy predictor is computationally straightforward and has showed excellent prediction capabilities. The predictor is thus well suited for NMPC purposes. Furthermore, the parameters of the neuro-fuzzy model can be argued to have explicit meaning, thus making the procedure of tuning the NMPC system more transparent when using the neuro-fuzzy predictor.

Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Systems, Man and Cybernetics
PublisherIEEE
Pages3459-3464
Number of pages6
Volume5
Publication statusPublished - 2000
Externally publishedYes
Event2000 IEEE International Conference on Systems, Man and Cybernetics - Nashville, TN, USA
Duration: 2000 Oct 82000 Oct 11

Other

Other2000 IEEE International Conference on Systems, Man and Cybernetics
CityNashville, TN, USA
Period00/10/800/10/11

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ASJC Scopus subject areas

  • Hardware and Architecture
  • Control and Systems Engineering

Cite this

Waller, J. B., Furuzuki, T., & Hirasawa, K. (2000). Nonlinear model predictive control utilizing a neuro-fuzzy predictor. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (Vol. 5, pp. 3459-3464). IEEE.