Download pdf
Tuning of FOPID parameters combined with swarm intelligent algorithm
- 1 Beijing Jiaotong University
* Author to whom correspondence should be addressed.
Abstract
This paper delves into the parameter tuning of fractional-order PID (FOPID) controllers. FOPID controllers, with additional integral and derivative orders compared to traditional PID controllers, possess enhanced capabilities in handling complex systems. However, effective tuning of its five parameters is challenging. To address this, multiple intelligent algorithms are investigated. The improved sparrow search algorithm (ISSA) utilizes Chebyshev chaotic mapping initialization, adaptive t-distribution, and the firefly algorithm to overcome the limitations of the basic algorithm, showing high accuracy, speed, and robustness in multi-modal problems. The grey wolf optimizer (GWO), inspired by the hunting behavior of grey wolves, has procedures for encircling, hunting, and attacking but may encounter local optima, and several improvement methods have been proposed. The genetic algorithm, based on the survival of the fittest principle, involves encoding, decoding, and other operations. Taking vehicle ABS control as an example, the genetic algorithm-based FOPID controller outperforms the traditional PID controller. In conclusion, different algorithms have their own advantages in FOPID parameter tuning, and the selection depends on system characteristics and control requirements. Future research can focus on further algorithm improvement and hybrid methods to achieve better control performance, providing a valuable reference for FOPID applications in industry.
Keywords
FOPID Controller, Improved Gray Wolf Optimization Algorithm, improved sparrow search algorithm, genetic algorithm
[1]. He, B., Li, L., Cheng, J. Y., & Zhou, X. (2024). Improve the grey wolf optimization algorithm for fractional PID controller parameter setting. Science and Technology Bulletin, 40(5), 39-45.
[2]. Chen, X., Wu, L., & Yang, X. (2024). Fractional-order PID parameter tuning based on an improved sparrow search algorithm. Control and Decision-making, 39(4), 1177-1184.
[3]. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69(1), 46-61.
[4]. Min, C., Cui, J., Zhao, C. C., Qiao, H., & Liu, F. (2024). Improved grey wolf optimization algorithm based on a nonlinear search strategy and its application. Journal of Sichuan Normal University (Natural Science Edition), 47(4), 537-547.
[5]. Jiang, Y., Fu, J., Gan, R. J., Sun, Y., & Wang, F. (2024). Improving the grey wolf algorithm to optimize the application of GBDT in PM₂.₅ prediction. Journal of Safety and Environment, 24(4), 1569-1580.
[6]. Liu, H., Lei, B., Wang, W., & Chai, H. (2023). Improved fusion genetic grey wolf optimization algorithm for solving the TSP problem. Computer Simulation, 40(9), 333-338.
[7]. Srinivas, M., & Patnaik, L. M. (1994). Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24(4), 656.
[8]. Chen, Y., Yang, M., Xu, L., & Liu, J. (2018). Vehicle ABS fractional PID control with genetic algorithm parameters setting. Automation in Manufacturing Industry, 40(1), 24-2739.
Cite this article
Lou,M. (2025). Tuning of FOPID parameters combined with swarm intelligent algorithm. Advances in Engineering Innovation,15,40-44.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Journal：Advances in Engineering Innovation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).