PID controller tuning for an AVR system using Particle Swarm Optimisation Techniques and Genetic Algorithm Techniques; A comparison based approach

Authors

  • Aliyu Sabo Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.
  • Mahmud Bawa Nigerian Defence Academy, Kaduna, Nigeria
  • Yunusa Yakubu Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.
  • Alan Audu Ngyarmunta Department of Electrical Engineering, Faculty of Engineering Technology, Nigerian Defense Academy, Kaduna Nigeria
  • Yunusa Aliyu Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.
  • Alama Musa Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.
  • Mohamed Katun Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

DOI:

https://doi.org/10.26740/vubeta.v2i2.36821

Keywords:

Proportional Integral Derivative (PID), Automatic Voltage Regulator, Particle Smarm Optimization (PSO), Genetic algorithm

Abstract

This paper presents the tuning of a Proportional-Integral-Derivative (PID) controller for an Automatic Voltage Regulator (AVR) system using a metaheuristic optimization technique. The aim is to enhance the system's dynamic response by minimizing overshoot, settling time, and steady-state error. Particle Swarm Optimization (PSO), a robust and widely applied metaheuristic technique, was selected due to its simplicity and efficiency in exploring the search space for optimal solutions. The AVR system was modelled and simulated using MATLAB and the performance of the optimized PID controller was analyzed and compared with a traditional manually tuned PID controller. The results show a significant improvement in system performance with the PSO-tuned PID controller, validating the potential of metaheuristic optimization for PID tuning in control systems.

Author Biographies

Aliyu Sabo, Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Engr. Dr. Sabo Aliyu (Ph.D.) MIEEE. PES. (Member, IEEE) completed his bachelor’s degree in electrical engineering from the Prestigious Ahmadu Bello University, Zaria, Kaduna State, Nigeria in 2011. Engr. Dr. Sabo Aliyu completed his M.Sc. and Ph.D. Degrees in Electrical Power Systems Engineering from the University of Putra Malaysia. His main research areas include the application of Neuro Fuzzy Controllers to power systems, Computational Intelligence techniques, Power System Oscillation Damping Controller Designs, Power Systems Optimizations, Power Flow and Optimal Power Flow, Power Quality, and Robust controllers with several online publications. Engr. Dr. Sabo Aliyu is currently a Senior Lecturer at the Nigerian Defence Academy, Kaduna State, Nigeria. He is a registered Engineer under the Council for the Regulation of Engineering in Nigeria (COREN). He can be contacted at email: saboaliyu98@gmail.com.

Mahmud Bawa, Nigerian Defence Academy, Kaduna, Nigeria

Department of Electrical Engineering, Faculty of Engineering Technology, Nigerian Defense Academy, Kaduna Nigeria

Yunusa Yakubu, Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Alan Audu Ngyarmunta , Department of Electrical Engineering, Faculty of Engineering Technology, Nigerian Defense Academy, Kaduna Nigeria

Department of Electrical Engineering, Faculty of Engineering Technology, Nigerian Defense Academy, Kaduna Nigeria

Yunusa Aliyu, Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Alama Musa, Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Mohamed Katun, Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

Department of Electrical Engineering, Faculty of Engineering and Engineering Technology, Nigerian Defence Academy (NDA), Kaduna, Nigeria.

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Published

2025-06-17

How to Cite

[1]
A. Sabo, “PID controller tuning for an AVR system using Particle Swarm Optimisation Techniques and Genetic Algorithm Techniques; A comparison based approach ”, Vokasi Unesa Bull. Eng. Technol. Appl. Sci., vol. 2, no. 2, pp. 270–280, Jun. 2025.

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Vol. 2 No. 2 (2025)

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Copyright (c) 2025 Aliyu Sabo, Mahmud Bawa, Yunusa Yakubu, Alan Audu Ngyarmunta , Yunusa Aliyu, Alama Musa, Mohamed Katun

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