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The proposed multi-region fuzzy logic controller and traditional PID controller were subjected to broad-based simulation tests in rapid prototyping software - Matlab/Simulink.
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In both cases, a model of steam generator which is readily available in literature was used for control algorithms synthesis purposes. The gains of the local PID controllers have been derived by solving appropriate optimization tasks with the cost function in a form of integrated squared error (ISE) criterion. In the paper a comparison of proposed multi region fuzzy logic controller and traditional PID controllers designed only for nominal condition is presented. Incorrect water level in the steam generator may lead to accidental shutdown of the nuclear reactor and consequently financial losses. Thus the steam generator is often controlled manually by operators. Control of water level in the steam generator conducted by a traditional PID controller which is designed for nominal power level of the nuclear reactor operates insufficiently well in wide range of operational conditions, especially at the low thermal power level. The U-tube steam generator has a nonlinear dynamics depending on thermal power transferred from coolant of the primary loop of the PWR plant. Yurkevich, Novosibirsk State Technical University, RussiaĪbstract In the paper, analysis of multi-region fuzzy logic controller with local PID controllers for steam generator of pressurized water reactor (PWR) working in wide range of thermal power changes is presented. Jan Węglarz, Poznań University of Technology, Poland Ryszard Tadeusiewicz, AGH University of Science and Technology, Poland Heinz Schaettler, Washington University, St Louis, USA Witold Respondek, Institut National des Sciences Appliquées de Rouen, FranceĮric Rogers, University of Southampton, UK Ian Postlethwaite, Newcastle University, Newcastle, UK Marek Pawełczyk, Silesian University of Technology, Poland Wojciech Mitkowski, AGH University of Science and Technology, Polandįrancisco Javier Muros, University of Seville, Spainīozenna Pasik-Duncan, University of Kansas, Lawrence, USA Krzysztof Malinowski, Warsaw University of Technology, Poland Mahmoud, KFUM, Dahram, Saudi ArabiaĮvgenii K. Urszula Ledzewicz, Southern Illinois University at Edwardsville, USA Irena Lasiecka, University of Virginia, USA Piotr Kulczycki, Institute of System Research PAS, Poland Józef Korbicz, University of Zielona Góra, Poland Jerzy Klamka, Institute of Theoretical and Applied Informatics PAS, Poland Marek Kimmel, Rice University Houston, USA Haddad, Georigia University, Atlanta, USAĪlberto Isidori, Università di Roma "La Sapienza" ItaliaĪsatur Zh. Mats Gyllenberg, University of Helsinki, Finland Marcelo D.Fragoso, LNCC/MCT, Rio de Janeiro, BrasilĪvner Friedman, MBI Ohio State University, Columbus, USA Moritz Diehl, University of Freiburg, Germany Reggie Davidrajuh, University of Stavanger, Norway Jacek Błażewicz, Poznań University of Technology, PolandĪdam Czornik, Silesian University of Technology, Poland Roman Barták, Charles University, Prague, Czech RepublicĪndrzej Bartoszewicz, Lodz University of Technology, Poland Sebastian Anita, University Alexandru Ioan Cuza, Romania
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Zbigniew Ogonowski, Silesian University of Technology, Gliwice, Poland Source Normalized Impact per Paper ( SNIP) 2020: 0.950Įditor-in-Chief prof. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.Īims and Scope: Archives of Control Sciences publishes papers in the broadly understood field of control science and related areas while promoting the closer integration of the Polish, as well as other Central and East European scientific communities with the international world of science. Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems.