Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger

Margarita Terziyska; Yancho Todorov; Michail Petrov

doi:10.3182/20061002-4-BG-4905.00016

Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger

Margarita Terziyska, Yancho Todorov, Michail Petrov

Not published at VTT

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

4 Citations (Scopus)

Abstract

It is presented in this paper an adaptive predictive supervisory algorithm to the temperature control of a heating system with a heat exchanger. The nonlinear predictive control strategy is designed on the basis of a Takagi-Sugeno fuzzy-neural model and a simple optimization procedure. An additional supervisory level in the control system is introduced for adaptive tuning of a weighting factor in the predefined optimization criterion. Using the proposed algorithm a higher system performance can be achieved which leads to reduction of the energy consumption into the heating system. The proposed approach is studied by experimental simulations to control a temperature in the heating system.

Original language	English
Title of host publication	1st IFAC Workshop on Energy Saving Control in Plants and Buildings
Publisher	Elsevier
Pages	93-98
ISBN (Print)	978-3-902661-19-7
DOIs	https://doi.org/10.3182/20061002-4-BG-4905.00016
Publication status	Published - 1 Dec 2006
MoE publication type	A4 Article in a conference publication
Event	International IFAC Workshop on Energy Saving Control in Plants and Buildings, ESC 2006 - Bansko, Bulgaria Duration: 2 Oct 2006 → 5 Oct 2006

Publication series

Series	IFAC Proceedings Volumes
Number	19
Volume	39
ISSN	1474-6670

Conference

Conference	International IFAC Workshop on Energy Saving Control in Plants and Buildings, ESC 2006
Country	Bulgaria
City	Bansko
Period	2/10/06 → 5/10/06

Keywords

Fuzzy control
Heating systems
Predictive control

UN SDGs

This output contributes to the following Sustainable Development Goal(s)

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10.3182/20061002-4-BG-4905.00016

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abstract = "It is presented in this paper an adaptive predictive supervisory algorithm to the temperature control of a heating system with a heat exchanger. The nonlinear predictive control strategy is designed on the basis of a Takagi-Sugeno fuzzy-neural model and a simple optimization procedure. An additional supervisory level in the control system is introduced for adaptive tuning of a weighting factor in the predefined optimization criterion. Using the proposed algorithm a higher system performance can be achieved which leads to reduction of the energy consumption into the heating system. The proposed approach is studied by experimental simulations to control a temperature in the heating system.",

keywords = "Fuzzy control, Heating systems, Predictive control",

author = "Margarita Terziyska and Yancho Todorov and Michail Petrov",

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address = "Netherlands",

note = "International IFAC Workshop on Energy Saving Control in Plants and Buildings, ESC 2006 ; Conference date: 02-10-2006 Through 05-10-2006",

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Terziyska, M, Todorov, Y & Petrov, M 2006, Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger. in 1st IFAC Workshop on Energy Saving Control in Plants and Buildings. Elsevier, IFAC Proceedings Volumes, no. 19, vol. 39, pp. 93-98, International IFAC Workshop on Energy Saving Control in Plants and Buildings, ESC 2006, Bansko, Bulgaria, 2/10/06. https://doi.org/10.3182/20061002-4-BG-4905.00016

Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger. / Terziyska, Margarita; Todorov, Yancho; Petrov, Michail.

1st IFAC Workshop on Energy Saving Control in Plants and Buildings. Elsevier, 2006. p. 93-98 (IFAC Proceedings Volumes; No. 19, Vol. 39).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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T1 - Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger

AU - Terziyska, Margarita

AU - Todorov, Yancho

AU - Petrov, Michail

PY - 2006/12/1

Y1 - 2006/12/1

N2 - It is presented in this paper an adaptive predictive supervisory algorithm to the temperature control of a heating system with a heat exchanger. The nonlinear predictive control strategy is designed on the basis of a Takagi-Sugeno fuzzy-neural model and a simple optimization procedure. An additional supervisory level in the control system is introduced for adaptive tuning of a weighting factor in the predefined optimization criterion. Using the proposed algorithm a higher system performance can be achieved which leads to reduction of the energy consumption into the heating system. The proposed approach is studied by experimental simulations to control a temperature in the heating system.

AB - It is presented in this paper an adaptive predictive supervisory algorithm to the temperature control of a heating system with a heat exchanger. The nonlinear predictive control strategy is designed on the basis of a Takagi-Sugeno fuzzy-neural model and a simple optimization procedure. An additional supervisory level in the control system is introduced for adaptive tuning of a weighting factor in the predefined optimization criterion. Using the proposed algorithm a higher system performance can be achieved which leads to reduction of the energy consumption into the heating system. The proposed approach is studied by experimental simulations to control a temperature in the heating system.

KW - Fuzzy control

KW - Heating systems

KW - Predictive control

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DO - 10.3182/20061002-4-BG-4905.00016

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SN - 978-3-902661-19-7

T3 - IFAC Proceedings Volumes

SP - 93

EP - 98

BT - 1st IFAC Workshop on Energy Saving Control in Plants and Buildings

PB - Elsevier

T2 - International IFAC Workshop on Energy Saving Control in Plants and Buildings, ESC 2006

Y2 - 2 October 2006 through 5 October 2006

ER -

Adaptive supervisory tuning of nonlinear model predictive controller for a heat exchanger

Abstract

Publication series

Conference

Keywords

UN SDGs

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