HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER FOR SETTING PID PARAMETERS OF BLDC MOTORS
Abstract
BLDC (Brushless DC) motors have advantages over asynchronous motors and dc motors in various aspects. Particularly in electric bicycles and flying cars, BLDC motors are utilized widely. Electric bicycles and flying cars are becoming increasingly popular, and as a result, the significance of BLDC motors and their cost-effective and efficient utilization has been growing rapidly. PID (Proportional Integral Derivative) controllers are generally used in motor control because they are cheap and perform well. Many methods have been used to adjust PID parameters. Although methods such as Ziegler-Nichols, Cohen-Coon etc. are widely used, there are also new methods such as optimization algorithms PSO (Particle Swarm Optimization), Whale Optimization Technique, Gray Wolf Optimization technique etc. The hybrid method: HPSOGWO (Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer) is a combination of PSO and GWO (Grey Wolf Optimizer) techniques, and it can be used for tuning PID parameters. As associated with this, the aim of this study is to show the superiority of HPSOGWO algorithm in optimizing the PID parameters. In the content of this study, the essentials regarding the optimization background, and details of the BLDC motor modeling was explained first. After that, the methodology of the hybrid solution was expressed and then the application phase was explained in detail, by including the results generally. In the context of the intelligent optimization approach of this study, the results were obtained in the MATLAB Simulink environment. The application of the used solution method revealed its superiority over the study conducted solely with GWO in various parameters.
References
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Abstract
References
- 1. Izza, A., Jamaaluddin, U., I, R., W, W., “Identification and implementation hybrid fuzzy logic and PID controller for speed control of BLDC motor”, Journal of Physics: Conference Series 4th Annual Applied Science and Engineering Conference, Bali, 2019.
- 2. Pallav, D., Santanu, K, N., “Grey Wolf Optimizer Based PID Controller for Speed Control of BLDC Motor”, Journal of Electrical Engineering & Technology, Vol. 16, Pages 955-961, 2021.
- 3. Embiruçu, M., Neto, A, C., “Tuning of PID Controllers: An Optimization-Based Method”, IFAC Proceedings Volumes, Vol. 33, Pages 367-372, 2000.
- 4. Neenu, T., P, P., “Position Control of DC Motor Using Genetic Algorithm Based PID Controller”, Proceedings of the World Congress on Engineering, London, 2009.
- 5. Loucif, F., Kechida, S., Sebbagh, A., “Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipülatör”, Journal of the Brazilian Society of Mechanical Sciences and Engineering, Vol. 42, 2019
- 6. Hsiao, Y., Chuang, C., Chien C., “Ant colony optimization for designing of PID controllers”, lntemational Symposium on Computer Aided Control Systems Design, Taiwan, 2004
- 7. Narinder, S., S, B, S., “Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance”, Journal of Applied Mathematics, Vol 2017.
- 8. The Electronics and Robotics Club, “PID Controller”, https://erc-bpgc.github.io/handbook/automation/ControlTheory/PID_Controller/, April 20, 2023
- 9. Riccardo, P., James, K., Tim B., “Particle swarm optimization”, Springer Science + Business Media, Vol. 1, Pages 33-57, 2007.
- 10. Seyedali, M., Jin S, S., “Multi-Objective Optimization using Artificial Intelligence Techniques”, Springer, Germany, 2020.
- 11. Yuhui, S., Russell C, E., “Empirical study of particle swarm optimization”, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, Washington DC, 1999.
- 12. Seyedali, M., Seyed, M, M., Andrew, L., “Grey Wolf Optimizer”, Advances in Engineering Software, Vol. 69, Pages 46-61, 2014.
- 13. Xin-She, Y., “Swarm intelligence based algorithms: a critical analysis”, Evolutionary Intelligence, Vol. 7, Pages 17-28, 2014.
- 14. Fatih, A, Ş., Fatih, G., Asım, S, Y., Tunncay Y., “A novel hybrid PSO–GWO algorithm for optimization problems”, Engineering with Computers, Vol. 35, Pages 1359–1373, 2019.
- 15. Duangjai, J., “A hybrid differential evolution with grey wolf optimizer for continuous global optimization”, International Conference on Information Technology and Electrical Engineering, Chiang Mai, 2015.
- 16. Mohamed, A, T., A, F, A., “A Hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function”, Memetic Computing, Vol. 9, Pages 347-359, 2017.
- 17. Narinder, S., Satya, B, S., “A novel hybrid GWO-SCA approach for optimization problems”, Engineering Science and Technology, an International Journal, Vol. 20, Pages 1586-1601, 2017
- 18. Shaheen, A, M, M., Hasanien, M, H., Alkuhayli, A., “A novel hybrid GWO-PSO optimization technique for optimal reactive power dispatch problem solution”, Ain Shams Engineering Journal, Vol. 12, Pages 621-630, 2021.
Details
| Primary Language | English |
|---|---|
| Subjects | Simulation, Modelling, and Programming of Mechatronics Systems |
| Journal Section | Research Article |
| Authors | |
| Publication Date | August 31, 2023 |
| Submission Date | July 3, 2023 |
| Published in Issue | Year 2023 Volume: 7 Issue: 2 |
Cite
International Journal of 3D Printing Technologies and Digital Industry is lisenced under Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı