Robust model predictive control of a nuclear power plant pressurizer subsystem

T. Péni, I. Varga, G. Szederkényi, J. Bokor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Robust model predictive control of an industrial pressurizer is presented in this paper. The physical model of the pressurizer is based on first engineering principles and the model parameters have been previously identified from measured data. To satisfy the hard constraints on the state variables and the input even in the presence of disturbances, the so-called single policy robust model predictive control method is applied. The maximal admissible level set, the disturbance invariant set and the terminal sets are determined for the system. Simulation results show that the proposed controller satisfies all the requirements and shows good time-domain behavior.

Original languageEnglish
Title of host publicationProceedings of the 25th IASTED International Conference on Modelling, Identification, and Control, MIC 2006
Pages167-172
Number of pages6
Publication statusPublished - Dec 1 2006
Event25th IASTED International Conference on Modelling, Identification, and Control, MIC 2006 - Lanzarote, Canary Islands, Spain
Duration: Jan 6 2006Jan 8 2006

Publication series

NameProceedings of the IASTED International Conference on Modelling, Identification, and Control, MIC
ISSN (Print)1025-8973

Other

Other25th IASTED International Conference on Modelling, Identification, and Control, MIC 2006
CountrySpain
CityLanzarote, Canary Islands
Period1/6/061/8/06

Keywords

  • Constrained control
  • Process control
  • Robust model predictive control

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Computer Science Applications

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  • Cite this

    Péni, T., Varga, I., Szederkényi, G., & Bokor, J. (2006). Robust model predictive control of a nuclear power plant pressurizer subsystem. In Proceedings of the 25th IASTED International Conference on Modelling, Identification, and Control, MIC 2006 (pp. 167-172). (Proceedings of the IASTED International Conference on Modelling, Identification, and Control, MIC).