Evolutionary Design of a PID Controller Using Metaheuristics Search Algorithms

Document Type : Original Article

Authors

1 Computer Science Department, Missouri State University, Springfield, MO 65807, USA

2 Department of Computer Science, Southern Connecticut State University, New Haven, CT, USA

3 Department of E.E.A., National Higher School of Technology and Engineering, 23005, Annaba, Algeria

4 Applied Science Department, Ajloun University College, Al-Balqa Applied University, Ajloun, Jordan

Abstract

Proportional-Integral-Derivative (PID) controllers are prominent due to their superior functionality and ease of use. However, optimizing their parameters presents a significant challenge. Adjusting parameters must be done carefully and cautiously because improper calibration can compromise the system’s stability. Although classic tuning techniques, such as the Ziegler-Nichols (ZN), are frequently employed, their efficiency is restricted due to the intricate and ever- changing nature of the systems, often leading to parameter settings that could be more optimal. Therefore, the need for a more accurate parameter-tuning technique is urgent. Various optimization strategies are used to fine-tune parameters with more precision. These methods include Gray Wolf Optimization (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). These methods are applied to fine-tune the PID parameters for a Direct Current (DC) motor to achieve optimal performance, and a comparative analysis of the results is conducted. Various fitness functions encompass performance metrics such as rise time, overshoot, peak time, settling time, and mean square error (MSE). These metrics are incorporated into the corresponding optimization approaches to quantitatively assess the controller’s performance. Various test cases have been utilized and the GA outperforms other algorithms ranging from 17% to 28% where rise time, settling time, and MSE are significant in the fitness function.

Keywords

References

[1]      Stephen Bassi Joseph, Emmanuel Gbenga Dada, Afeez Abidemi, David Opeoluwa Oyewola, and Ban Mohammed Khammas. Metaheuristic algorithms for PID controller parameters tuning: Review, approaches and open problems. Heliyon, page e09399, 2022.
[2]      DheerajReddy Maddi, Alaa Sheta, Dharani Davineni, and Heba Al-Hiary. Optimization of PID controller gain using evolutionary algorithm and swarm intelligence. In 2019 10th International Conference on Information and Communication Systems (ICICS), pages 199–204. IEEE, 2019.
[3]      Alaa Sheta, Malik S Braik, and Sultan Aljahdali. Genetic algorithms: a tool for image segmentation. In 2012 international conference on multimedia computing and systems, pages 84–90. IEEE, 2012.
[4]    James Kennedy. Particle swarm optimization. encyclopedia of machine learning. Springer, 760:766, 2011.
[5]    Malik Braik and Alaa Sheta.  Exploration of genetic algorithms and particle swarm optimization in improving the quality of medical images. Computational intelligence techniques in handling image processing and pattern recognition. Lambert Academic Publishing (LAP), Germany, pages 329–360, 2011.
[6]    Mirza Muhammad Sabir and Junaid Ali Khan. Optimal design of pid controller for the speed control of dc motor by using metaheuristic techniques. Advances in artificial neural systems, 2014, 2014.
[7]   J.G. Ziegler and N.B. Nichols. Optimum settings for automatic controllers.  Transactions of the American Society of Mechanical Engineers, 64(7):759–768, 1942.
[8]   Mahmud Iwan Solihin, Lee Fook Tack, and Moey Leap Kean. Tuning of PID controller using particle swarm optimization (PSO). In Proceeding of the international conference on advanced science, engineering and information technology, volume 1, pages 458–461, 2011.
[9]   N Kundariya and J Ohri. Design of intelligent PID controller using particle swarm optimization with different performance indices. International Journal of Scientific & Engineering Research, 4(7):1191–1194, 2013.
[10]    K Latha, V Rajinikanth, and PM Surekha. PSO- based PID controller design for a class of stable and unstable systems. International Scholarly Research Notices, 2013, 2013.
[11]    PM Meshram and Rohit G Kanojiya. Tuning of PID controller using ziegler-nichols method for speed control of DC motor. In IEEE-international conference on advances in engineering, science and management (ICAESM-2012), pages 117–122. IEEE, 2012.
[12]   Saeed Abedini and Hassan Zarabadipour. Tuning of an optimal PID controller with iterative feedback tuning method for DC motor. In The 2nd International Conference on Control, Instrumentation and Automation, pages 611–615. IEEE, 2011.
[13]   Marco Dorigo, Mauro Birattari, and Thomas Stutzle. Ant colony optimization. IEEE computational intelligence magazine, 1(4):28–39, 2006.
[14]   Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis.  Grey wolf optimizer. Advances in engineering software, 69:46–61, 2014
[15]   Stephen Bassi Joseph, Emmanuel Gbenga Dada, Afeez Abidemi, David Opeoluwa Oyewola, and Ban Mohammed Khammas. Metaheuristic algorithms for pid controller parameters tuning: review, approaches and open problems. Heliyon, 8(5):e09399, 2022.
[16]   Hang Wu, Weihua Su, and Zhiguo Liu. PID controllers: Design and tuning methods. In 2014 9th IEEE Conference on industrial electronics and applications, pages 808–813. IEEE, 2014.
[17]   Ahmed M. Nassef, Mohammad Ali Abdelkareem, Hussein M. Maghrabie, and Ahmad Baroutaji. Metaheuristic-based algorithms for optimizing fractional-order controllers—a recent, systematic, and comprehensive review. Fractal and Fractional, 7(7), 2023.
[18]   Liu Nanping, Xia Kewen, Zhou Juan, and Ge Chun. Numerical simulation on transistor with cqpso algorithm. In 2009 4th IEEE Conference on Industrial Electronics and Applications, pages 3732–3736. IEEE, 2009

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