Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Jonas B. Waller, Jinglu Hu, Kotaro Hirasawa

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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
Pages (from-to)3459-3464
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2000
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture


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