Evolutionary Design of a PID Controller Using Metaheuristics Search Algorithms | ||||
| Journal of Computing and Communication | ||||
| Article 3, Volume 4, Issue 1, February 2025, Page 31-42 PDF (1.38 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jocc.2025.411111 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Vennapusa M. Kumar Reddy1; Vennapusa M. Kumar Reddy1; Karnati Suresh1; Alaa Sheta2; walaa hassan ; Adel Djellal3; Abdel karim M. Baareh4
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| 1Computer Science Department, Missouri State University, Springfield, MO 65807, USA | ||||
| 2Department of Computer Science, Southern Connecticut State University, New Haven, CT, USA | ||||
| 3Department of E.E.A., National Higher School of Technology and Engineering, 23005, Annaba, Algeria | ||||
| 4Applied 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 | ||||
| PID Controller; Swarm Intelligence Algorithm; Evolutionary Algorithm; Metaheuristic Search Algorithms | ||||
| References | ||||
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