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. The U-tube steam generator has a nonlinear dynamics depending on thermal power transferred from coolant of the primary loop of the PWR plant. 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. Thus the steam generator is often controlled manually by operators. Incorrect water level in the steam generator may lead to accidental shutdown of the nuclear reactor and consequently financial losses. In the paper a comparison of proposed multi region fuzzy logic controller and traditional PID controllers designed only for nominal condition is presented. 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 both cases, a model of steam generator which is readily available in literature was used for control algorithms synthesis purposes. The proposed multi-region fuzzy logic controller and traditional PID controller were subjected to broad-based simulation tests in rapid prototyping software - Matlab/Simulink. These tests proved the advantage of multi-region fuzzy logic controller with local PID controllers over its traditional counterpart.
 G.R. Ansarifar, H.A. Talebi and H. Davilu: Adaptive estimator-based dynamic sliding mode control for the water level of nuclear steam generators. Progress in Nuclear Energy, 56 (2012), 61-70.
 R.N. Banavar and U.V. Deshpande: Robust controller design for a nuclear power plant using H∞ optimization. Proc. of the 35th IEEE Conf. on Decision and Control, 4 (1996), 4474-4479.
 J. Dobosz, K. Duzinkiewicz, A. Michalak and K. Wąsek: Technical report: Statics and Dynamics Simulation Model of Steam Generator of VVER-440 Nuclear Reactor. Gdańsk University of Technology, Institute of Electroenergetics and Automatics, Gdańsk, 1989, (in Polish).
 A. Fakhrazari and M. Boroushaki: Adaptive critic-based neurofuzzy controller for the steam generator water level. IEEE Trans. on Nuclear Science, 55(3), (2008), 1678-1685.
 Gee Yong Park and Poong Hyun Seong: Application of a fuzzy learning algorithm to nuclear steam generator level control. Annals of Nuclear Energy, 22(3-4), (1995), 135-146.
 H. Habibiya n, S Setay eshi and H. Arab-Alibeik: A fuzzy-gain-scheduled neural controller for nuclear steam generators. Annals of Nuclear Energy, 31(15), (2004), 1765-1781.
 A.M. Hasanul Basher and J. March-Leuba: Report No.: ORNL/TM-2001/166 Development of a Robust Model-Based Water Level Controller for U-Tube Steam Generator. Oak Ridge National Laboratory, Oak Ridge, Tennessee, 2001.
 E. Irving and C. Bihoreaux: Adaptive control of non-minimum phase systems Application to the P.W.R. steam generator water level control. 19th IEEE Conf. on Decision and Control, including The Symp. on Adaptive Processes, (1980), 274-279.
 Ke Hu and Jingqi Yuan: Multi-model predictive control method for nuclear steam generator water level. Energy Conversion and Management, 49(5), (2008), 1167-1174.
 M.V. Kothare, B. Mettler, M. Morari, P. Bendotti and C-M. Falinower: Linear parameter varying model predictive control for steam generator level control. Computers & Chemical Engineering, 21 (1997), Supplement, 861-866.
 S.R. Munasinghe, Min-Soeng Kim and Ju-Jang Lee: Adaptive neurofuzzy controller to regulate UTSG water level in nuclear power plants. IEEE Trans. on Nuclear Science, 52(1), (2005), 421-429.
 Myung-Ki Kim, Myoung Ho Shin and Myung Jin Chung: A gain-scheduled L2 control to nuclear steam generator water level. Annals of Nuclear Energy, 26(10), (1999), 905-916.
 Shou-Yu Cheng and Xin-Kai Liu: A new control strategy based on fuzzy-PID and water mass inventory for Nuclear Steam Generators. 2011 Int. Conf. on Machine Learning and Cybernetics (ICMLC), (2011), 1151-1155.