Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Jonas B. Waller*, Jinglu Hu, Kotaro Hirasawa

*この研究の対応する著者

研究成果査読

抄録

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.

本文言語English
ページ(範囲)39-44
ページ数6
ジャーナルResearch Reports on Information Science and Electrical Engineering of Kyushu University
6
1
出版ステータスPublished - 2001 12 1
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 電子工学および電気工学

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