Adaptive Neuro-Fuzzy Self Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor

(1) Hany Abdelfattah Mail (Suez University, Egypt)
(2) Said A. Kotb Mail (Egyptian Atomic Energy Authority (EAEA), Egypt)
(3) Mohamed Esmail Mail (Suez University, Egypt)
(4) * Mohamed I. Mosaad Mail (Yanbu Industrial College, Saudi Arabia)
*corresponding author


There has been a lot of interest in generating electricity using nuclear energy recently. This interest is due to the features of such a source of energy. The main part of the nuclear energy system is the reactor core, especially the most widely used Pressurized Water Reactor (PWR). This reactor is the hottest part of the nuclear system; security risks and economic possibilities must be considered. Controlling this reactor can increase the security and efficiency of nuclear power systems. This study presents a dynamic model of the (PWR), including the reactor's core, the plenums of the upper and lower, and the connecting piping between the reactor core and steam generator. In addition, an adaptive neuro-fuzzy (ANFIS) self-tuning PID Controller for the nuclear core reactor is presented. This adaptive controller is used to enhance the performance characteristics of PWR by supporting the profile of the reactor power, the coolant fuel, and hot leg temperatures. The suggested proposed ANFIS self-tuning controller is estimated through a comparison with the conventional PID, neural network, and fuzzy self-tuning controllers. The results showed that the proposed controller is best over traditional PID, neural network, and fuzzy self-tuning controllers. All simulations are throughout by using MATLAB/SIMULINK.


Nuclear energy; adaptive control; Adaptive Neuro-Fuzzy



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[1] R. V. V. Petrescu, A. Raffaella, S. Kozaitis, A. Antonio, and F. I. T. Petrescu, “Some Proposed Solutions to Achieve Nuclear Fusion,” American Journal of Engineering and Applied Sciences, vol. 10, no. 3, pp. 703-708, 2017,

[2] D. Halliday and R. Robert, Physics, Part II. 1st Edn., John Wiley and Sons, Inc., New York, 1966.

[3] OECD International Energy Agency, World Energy Outlook, 2021.

[4] OECD International Energy Agency Statistics,

[5] B. R. Upadhyaya, M. R. Lish, J. W. Hines, R. A. Tarver, "Instrumentation and control strategies for an integral pressurized water reactor," Nucl. Eng. Technol., vol. 47, pp. 148-156, 2015,

[6] M. F. El-Naggar, M. I. Mosaad, H. M. Hasanien, T. A. AbdulFattah, A. F. Bendary, “Elephant herding algorithm-based optimal PI controller for LVRT enhancement of wind energy conversion systems,” Ain Shams Engineering Journal, vol. 12, no. 1, 2021, pp. 599-608,

[7] M. I. Mosaad, H. S. Ramadan, M. Aljohani, M. F. El-Naggar and S. S. M. Ghoneim, "Near-Optimal PI Controllers of STATCOM for Efficient Hybrid Renewable Power System," IEEE Access, vol. 9, pp. 34119-34130,

[8] S. M. H. Mousakazemi, N. Ayoobian, G. R. Ansarifar, “Control of the reactor core power in PWR using optimized PID controller with the real-coded GA,” Ann. Nucl. Energy, vol. 118, pp. 107-121, 2018,

[9] R. Damayanti, A. Halim, and S. Bakhri, "Control of Air Pressurizer Levels on Pressurized Water Reactor (PWR) with Fractional Order PID Control System," (JPSE) Journal of Physical Science and Engineering, vol. 5, no. 2, pp. 71–82, 2020,

[10] Al M. H. Fayiz, "Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants," Journal of Physics: Conf. Series, vol. 781, p. 012052, 2017,

[11] X. Luan, J. Wang, Z. Yang, J. Zhou, “Load-following control of nuclear reactors based on fuzzy input-output model," Annals of Nuclear Energy, vol. 151, 2021, p. 107857,

[12] J. Ma, J. Fan, L. Lv, and L. Ma, "Reactivity estimation of nuclear reactor combined with neural network and mechanism model," 2012 IEEE Power and Energy Society General Meeting, 2012,

[13] Narrendar R. C. and Tilak, “Fuzzy Logic Based Reactivity Control in Nuclear Power Plants," International Journal of Innovative Research in Science, Engineering and Technology, vol. 3, no. 11, pp. 17139-17145, 2014,

[14] A. Naimi, J. Deng, A. Abdulrahman, V. Vajpayee, V. Becerra and N. Bausch, "Dynamic Neural Network-based System Identification of a Pressurized Water Reactor," 2020 8th International Conference on Control, Mechatronics and Automation (ICCMA), 2020, pp. 100-104,

[15] C. Liu, J.-F. Peng, F.-Y. Zhao, and C. Li, "Design and optimization of fuzzy-PID controller for the nuclear reactor power control," Nuclear Engineering and Design, vol. 239, no. 11, pp. 2311-2316, 2009,

[16] Santhiya M and Pappa N, "Rule Based Controller for Pressurized Water Type Nuclear Reactor," International Journal of Scientific & Technology Research, vol. 9, no. 2, 2020,

[17] A. A. Abouelsoud, H. Abdelfattah, M. Al Metwally, M. Nasr, “Stabilization of nuclear reactor kinetics with neutron and reactivity constraints,” The Mediterranean Journal of Measurement and Control., vol. 6, no. 3, pp. 82-88, 2010.

[18] H. Deol and H. A. Gabbar, "Self-tuning fuzzy logic PID controller, applications in nuclear power plants," International Journal of Intelligent Systems Technologies and Applications, vol. 14, no. 1, 2015, pp 70-89,

[19] M. Naghedolfeizi, "Dynamic Modeling of Pressurized Water Reactor Plant for Diagnostics and Control," University of Tennessee, 1990,

[20] G. Ellis, Control System Design Guide: Using Your Computer to Understand and Diagnose Feedback Controllers, Butterworth-Heinemann, 2012,

[21] Al M. H. Fayiz, "Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants," Journal of Physics: Conf. Series, vol. 781, p. 012052, 2017,

[22] A. A. Abouelsoud, H. Abdelfattah, M. El Metwally, and M. Nasr, “State feedback controller of Robinson nuclear plant with states and control constraints,” Nonlinear Dynamics and Systems Theory Journal, vol. 12, no. 1, pp. 1-17, 2012,

[23] M. Elsisi and H. Abdelfattah “New design of variable structure control based on lightning search algorithm for nuclear reactor power system considering load-following operation," Nuclear Engineering and Technology, vol. 52, no. 3, pp. 544-551, 2020,

[24] G. Li, X. Wang, B. Liang, X. Li, B. Zhang, and Y. Zou, "Modeling and control of nuclear reactor cores for electricity generation, A review of advanced technologies," Renew. Sustain. Energy Rev., vol. 60, pp. 116-128, 2016,

[25] G. J. Wu, B. Zeng, Z. Xu, W. Wu, and X. Ma, "State-space model predictive control method for core power control in pressurized water reactor nuclear power stations," Nucl. Eng. Technol., vol. 49, no. 1, pp. 134-140, 2017,

[26] M. I. Mossad, N. Sabiha, A. Abu-Siada and I. B. M. Taha, "Application of Superconductors to Suppress Ferroresonance Overvoltage in DFIG-WECS," IEEE Transactions on Energy Conversion, vol. 37, no. 2, pp. 766-777,

[27] A. Alhejji and M. I. Mosaad, “Performance enhancement of grid-connected PV systems using adaptive reference PI controller,” Ain Shams Engineering Journal, vol. 12, no. 1, pp. 541-554, 2021,

[28] M. F. M. Algreer and Y. R. M. Kuraz, "Design Fuzzy Self Tuning of PID Controller for Chopper-Fed DC Motor Drive," Al-Rafidain Engineering, vol. 16, no. 2, 2008,

[29] H. Abdelfattah, M. I. Mosaad, N. F Ibrahim, “Adaptive Neuro Fuzzy Technique for Speed Control of Six-Step Brushless DC Motor,” Indonesian Journal of Electrical Engineering and Informatics (IJEEI), vol. 9, no. 2, pp. 302-312, 2021,


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