PSO based Hybrid PID-FLC Sugeno Control for Excitation System of Large Synchronous Motor

Hung Quoc Duong, Quang Hong Nguyen, Duy Tien Nguyen, Lanh Van Nguyen


This paper proposes a hybrid control system integrating a PID controller and a fuzzy logic controller, using the particle swarm optimization (PSO) algorithm to optimize control parameters. The control object is an excitation system for a large synchronous motor, which is widely used in large power transmission systems. In practice, the change in load and excitation source can affect the operating mode of the motor. Therefore, a hybrid controller is designed to stabilize the power factor, resulting in better working performance. In the control algorithm, a PID controller is initially designed using PSO to optimize the control coefficients. The FLC-Sugeno control is then integrated with the PID, in which PSO is utilized to optimize membership functions. Numerical simulation results demonstrate the advantages of the proposed approach.


Doi: 10.28991/ESJ-2022-06-02-01

Full Text: PDF


Synchronous Motor; Excitation System; Particle Swarm Optimization; PSO; Fuzzy Logic Controller.


Torrey, D. A. (2020). Chapter 1: Electric Machine Fundamentals. Compendium on Electromagnetic Analysis, 1–119. doi:10.1142/9789813270282_0001.

Say, M. G., & Say, M. G. (1995). Performance and design of AC machines. CBS Publishers, New Delhi, India.

Barns, B. L. (2009). Electrical Machinery. Transactions of the American Institute of Electrical Engineers, XLV, 830–842. doi:10.1109/t-aiee.1926.5061280.

Horvath, B. (2009). Synchronous Motors & Sync Excitation Systems. In Western Mining Electrical Association; TM GE Automation Systems, Rapid City, North Dakota, United States. Available online: (accessed on January 2022).

Al-Hamrani, M. M., Von Jouanne, A., & Wallace, A. (2002). Power factor correction in industrial facilities using adaptive excitation control of synchronous machines. IEEE Conference Record of Annual Pulp and Paper Industry Technical Conference, 148–154. doi:10.1109/PAPCON.2002.1015143.

WEG group. (2021). The ABC’s of Synchronous Motors: For the Mining Industry. At. Available online: (accessed on December 2021).

Araki, M. (2009). PID control; Control systems, robotics, and Automation. In ©Encyclopedia of Life Support Systems (EOLSS), Vol. II. Available online: Chapters/C18/E6-43-03-03.pdf (accessed on December 2021).

Ziegler, J. G., & Nichols, N. B. (1942). Optimum settings for automatic controllers. Transactions of the ASME, 64(11). American Society of Mechanical Engineers (ASME), New York, United States

Allu, N., & Toding, A. (2020). Tuning with Ziegler Nichols Method for Design PID Controller at Rotate Speed DC Motor. In IOP Conference Series: Materials Science and Engineering, 846(1). doi:10.1088/1757-899X/846/1/012046.

Bickramdass, R., Persad, P., & Loutan Jr., K. (2021). Evaluation of an Anthropometric Fast Bowling Machine. HighTech and Innovation Journal, 2(2), 108–119. doi:10.28991/hij-2021-02-02-04.

Jondhale, A. S., Gaikwad, V. J., & Jondhale, S. R. (2015). Level Control of Tank System using PID Controller-A Review. IJSRD- International Journal for Scientific Research & Development, 3(10), 3(10), 636–638.

Brenna, M., Foiadelli, F., & Zaninelli, D. (2010). New stability analysis for tuning PI controller of power converters in railway application. IEEE Transactions on Industrial Electronics, 58(2), 533-543. doi:10.1109/TIE.2010.2047823.

Gani, M. M., Islam, M. S., & Ullah, M. A. (2019). Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm. SN Applied Sciences, 1(8). doi:10.1007/s42452-019-0929-y.

Tamalouzt, S., Belkhier, Y., Sahri, Y., Bajaj, M., Ullah, N., Chowdhury, M. S., Titseesang, T., & Techato, K. (2021). Enhanced direct reactive power control-based multi-level inverter for dfig wind system under variable speeds. Sustainability (Switzerland), 13(16), 9060. doi:10.3390/su13169060.

Pengpraderm, S., Kraikitrat, K., & Ruangsinchaiwanich, S. (2017). Automatic control of synchronous motor using PI controller for improving power factor. Interdisciplinary Research Review, 12(5), 35-41.

Al-Kababji, M. F., & Al-Sammak, A. N. B. (2002). Modeling & simulation of synchronous machine controlled by PID control for the reactive power compensation. The 6th Jordanian International Electrical & Electronics Engineering Conference JIEEEC, Amman, Jordan, 1-8.

Baygi, S. M. H., Karsaz, A., & Elahi, A. (2018). A hybrid optimal PID-Fuzzy control design for seismic exited structural system against earthquake: A salp swarm algorithm. In 2018 6th Iranian Joint Congress on Fuzzy and Intelligent Systems, CFIS 2018 (Vols. 2018-January, pp. 220–225). doi:10.1109/CFIS.2018.8336659.

Wu, C., Liu, J., Jing, X., Li, H., & Wu, L. (2017). Adaptive fuzzy control for nonlinear networked control systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(8), 2420–2430. doi:10.1109/TSMC.2017.2678760.

Paul, S., Arunachalam, A., Khodadad, D., & Rubanenko, O. (2020). Fuzzy Tuned PID Controller for Vibration Control of Agricultural Manipulator. In HORA 2020 - 2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings (pp. 1–5). IEEE. doi:10.1109/HORA49412.2020.9152848.

Boudia, A., Messalti, S., Harrag, A., & Boukhnifer, M. (2021). New hybrid photovoltaic system connected to superconducting magnetic energy storage controlled by PID-fuzzy controller. Energy Conversion and Management, 244, 114435. doi:10.1016/j.enconman.2021.114435.

Kılıç, A., & Altaş, I. (1996). Power Factor Correction of Synchronous Motor Using Fuzzy Logic. Mathematical and Computational Applications, 1(1), 66–72. doi:10.3390/mca1010066.

Keçecioğlu, Ö. F., Açikgöz, H., Yildiz, C., Şekkeli, M., & Gani, A. (2016). Simulation Study on Power Factor Correction Controlling Excitation Current of Synchronous Motor with Fuzzy Logic Controller. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 229–233. doi:10.18201/ijisae.2016specialissue-146979.

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, IEEE. Perth, WA, Australia. doi:10.1109/icnn.1995.488968

Zeng, W., Zhu, W., Hui, T., Chen, L., Xie, J., & Yu, T. (2020). An IMC-PID controller with Particle Swarm Optimization algorithm for MSBR core power control. Nuclear Engineering and Design, 360, 110513. doi:10.1016/j.nucengdes.2020.110513.

Kaukonen, J. (1999). Salient pole synchronous machine modelling in an industrial direct torque controlled driver application. PhD Thesis, Lappeenranta University of Technology, Lappeenranta, Finland.

Khanh, B. Q., Vinh, D. Q., Dang, P. Q., & Dich, N. Q. (2020). Industrial electric drive control. Scientific and technical Publishers, 1-24.

Kuo, I. H., Horng, S. J., Kao, T. W., Lin, T. L., Lee, C. L., & Pan, Y. (2009). An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization. Expert Systems with Applications, 36(3 PART 2), 6108–6117. doi:10.1016/j.eswa.2008.07.043.

Farh, H. M. H., Eltamaly, A. M., & Othman, M. F. (2018). Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system. PLoS ONE, 13(11). doi:10.1371/journal.pone.0206171.

Rahma, A., & Khemliche, M. (2014). Combined approach between FLC and PSO to find the best MFs to improve the performance of PV system. 2014 International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2014. doi:10.1109/CISTEM.2014.7077038.

Anitha, T., Gopu, G., Nagarajapandian, M., & Devan, P. A. M. (2019). Hybrid Fuzzy PID Controller for Pressure Process Control Application. 2019 IEEE Student Conference on Research and Development, SCOReD 2019, 129–133. doi:10.1109/SCORED.2019.8896276.

Brehm, T., & Rattan, K. S. (1994). Hybrid fuzzy logic PID controller. IEEE International Conference on Fuzzy Systems, 3, 1682–1687. doi:10.1109/naecon.1993.290839.

Gomaa Haroun, A. H., & Li, Y. Ya. (2017). A novel optimized hybrid fuzzy logic intelligent PID controller for an interconnected multi-area power system with physical constraints and boiler dynamics. ISA Transactions, 71, 364–379. doi:10.1016/j.isatra.2017.09.003.

Erenoglu, I., Eksin, I., Yesil, E., & Guzelkaya, M. (2006). An intelligent hybrid fuzzy PID controller. 20th European Conference on Modelling and Simulation: Modelling Methodologies and Simulation Key Technologies in Academia and Industry, ECMS 2006, 62–66. doi:10.7148/2006-0062.

Torreglosa, J. P., Jurado, F., Garca, P., & Fernndez, L. M. (2011). Application of cascade and fuzzy logic based control in a model of a fuel-cell hybrid tramway. Engineering Applications of Artificial Intelligence, 24(1), 1–11. doi:10.1016/j.engappai.2010.08.009.

Full Text: PDF

DOI: 10.28991/ESJ-2022-06-02-01


  • There are currently no refbacks.

Copyright (c) 2022 Hung Quoc Duong, Hong Quang Nguyen, Duy Tien Nguyen, Lanh Van Nguyen