Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Jonas B. Waller, Jinglu Hu, Kotaro Hirasawa

Research output: Contribution to journalArticlepeer-review

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
Pages (from-to)3459-3464
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume5
DOIs
Publication statusPublished - 2000

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Fingerprint Dive into the research topics of 'Nonlinear model predictive control utilizing a neuro-fuzzy predictor'. Together they form a unique fingerprint.

Cite this