Open AccessNonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant
Author(s) -
Xiangjie Liu,
Mengyue Wang
Publication year - 2014
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2014/908526
Subject(s) - nuclear power plant , nonlinear system , pressurized water reactor , control theory (sociology) , controller (irrigation) , fuzzy logic , model predictive control , pid controller , compensation (psychology) , nuclear reactor , constraint (computer aided design) , nuclear power , engineering , power (physics) , control engineering , scheme (mathematics) , computer science , control (management) , temperature control , nuclear engineering , mathematics , mechanical engineering , artificial intelligence , psychology , agronomy , ecology , mathematical analysis , physics , quantum mechanics , nuclear physics , psychoanalysis , biology
Reliable power and temperature control in pressurized water reactor (PWR) nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC), by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC) scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation
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