Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Jonas B. Waller; Jinglu Hu; Kotaro Hirasawa

doi:10.1109/ICSMC.2000.886544

Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Jonas B. Waller, Jinglu Hu, Kotaro Hirasawa

Research output: Contribution to journal › Article › peer-review

8 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 language	English
Pages (from-to)	3459-3464
Number of pages	6
Journal	Proceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume	5
DOIs	https://doi.org/10.1109/ICSMC.2000.886544
Publication status	Published - 2000
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Hardware and Architecture

Access to Document

10.1109/ICSMC.2000.886544

Cite this

@article{359b619f2e6f4dec812e81774909d9ac,

title = "Nonlinear model predictive control utilizing a neuro-fuzzy predictor",

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.",

author = "Waller, {Jonas B.} and Jinglu Hu and Kotaro Hirasawa",

year = "2000",

doi = "10.1109/ICSMC.2000.886544",

language = "English",

volume = "5",

pages = "3459--3464",

journal = "Proceedings of the IEEE International Conference on Systems, Man and Cybernetics",

issn = "0884-3627",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Nonlinear model predictive control utilizing a neuro-fuzzy predictor

AU - Waller, Jonas B.

AU - Hu, Jinglu

AU - Hirasawa, Kotaro

PY - 2000

Y1 - 2000

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0034506476&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034506476&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2000.886544

DO - 10.1109/ICSMC.2000.886544

M3 - Article

AN - SCOPUS:0034506476

SN - 0884-3627

VL - 5

SP - 3459

EP - 3464

JO - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

JF - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

ER -

Nonlinear model predictive control utilizing a neuro-fuzzy predictor

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this