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

Margarita Terziyska, Yancho Todorov, Michail Petrov

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-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 languageEnglish
Pages (from-to)93-98
JournalIFAC Proceedings Volumes
Volume39
Issue number19
DOIs
Publication statusPublished - 1 Dec 2006
MoE publication typeA4 Article in a conference publication
EventInternational IFAC Workshop on Energy Saving Control in Plants and Buildings, ESC 2006 - Bansko, Bulgaria
Duration: 2 Oct 20065 Oct 2006

Fingerprint

Heat exchangers
Tuning
Heating
Controllers
Temperature control
Energy utilization
Control systems
Temperature

Keywords

  • Fuzzy control
  • Heating systems
  • Predictive control

Cite this

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

In: IFAC Proceedings Volumes, Vol. 39, No. 19, 01.12.2006, p. 93-98.

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

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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.

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