Buy (600 RUB)
Generate QR code
Open Access
Subscription or Fee Access
PID-Controller Parameters Optimization of a Brushless DC Motor Using the 3SO Algorithm
https://doi.org/10.17587/mau.26.480-487
Abstract
To improve the speed control of brushless DC motors (BLDCs), a method for tuning the PID-controller parameters based on the metaheuristic algorithm 3S Optimizer (3SO) is proposed. BLDCs are worthy competitors to AC drives due to the absence of brushes and a commutator. Electronic commutation of the motor ensures high operating speed, and the behavior is controlled by digital systems. The accuracy of BLDC speed control is of paramount importance to ensure the efficient operation of equipment, devices and products in which the motor is built. PID-controllers are widely used in industrial applications due to their efficiency, simplicity and versatility. However, empirical strategies for tuning PID-controller parameters are not always optimal. Ma ny methods based on metaheuristic algorithms have been proposed for fine-tuning, allowing the control system to learn and adjust parameters in real time, effectively responding to environmental changes. The method proposed in this paper is based on the recently proposed metaheuristic algorithm 3S Optimizer. The method solves the problems of slow convergence and low accuracy, which are typical for traditional PID-controllers. The integral of the squared error is chosen as the objective function of optimization. The following parameters are chosen as constraints: overshoot, transient time, and rise time. Constraints are included in the objective function as penalties. Thus, the problem of minimizing a function with constraints is reduced to the problem of finding the minimum of a function without constraints. The PID-controller optimization program is implemented in the MATLAB environment. For modeling, the Permanent Magnet Synchronous Machine BLDC motor blocks and the Universal Bridge inverter block, available in Simulink, were used. The experiments conducted, which involved a PID-controller with non-optimized parameters and controllers optimized by the 3SO algorithm and a genetic algorithm, showed the superiority of our method.
About the Authors
X. D. Mai
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
X. D. Mai
Tomsk, 634050
I. A. Hodashinsky
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
Hodashinsky I. A., Dr. of Tech. Sc., Professor
Tomsk, 634050
Yu. A. Shurygin
Tomsk State University of Control Systems and Radioelectronics
Russian Federation
Russian Federation
Yu. A. Shurygin
Tomsk, 634050
References
1. Mohanraj D., Aruldavid R., Verma R., Sathiyasekar K., Barnawi A. B., Chokkalingam B., Mihet-popa L. A Review of BLDC Motor: State of Art, Advanced Control Techniques, and Applications, IEEE Access, 2022, vol. 10, pp. 54833—54869.
2. Wang Z., Zhang Y., Yu P., Cao N., Dintera H. Speed Control of Motor Based on Improved Glowworm Swarm Optimization, Computers, Materials and Continua, 2021, vol. 69, pp. 503—519.
3. Ibrahim H. E. A., Hassan F. N., Shomer A. O. Optimal PID control of a brushless DC motor using PSO and BF techniques, Ain Shams Engineering Journal, 2014, vol. 5, pp. 391—398.
4. Filimonov A. B., Filimonov N. B. Sertain Problematic Aspects of Fuzzy PID Regulation, Mekhatronika, Avtomatizatsiya, Upravlenie, 2018, vol. 19, no. 12, pp. 762—769 (In Russian).
5. Filimonov A. B., Filimonov N. B. On the issue of constructing fuzzy PID controllers, Mekhatronika, Avtomatizatsiya, Upravlenie, 2018, no 2, pp. 112—116 (In Russian).
6. Sablina G. V., Markova V. A. Tuning a PID controller in a system with a delayed second-order object, Optoelectronics, Instrumentation and Data Processing, 2022, vol. 58, no. 4, pp. 410—417.
7. El-samahy A. A., Shamseldin M. A. Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control, Ain Shams Engineering Journal, 2018, vol. 9, pp. 341—352.
8. Sanguino T. J. M. Dominguez J. M. L. Design and stabilization of a Coanda effect-based UAV: Comparative study between fuzzy logic and PID control approaches, Robotics and Autonomous Systems, 2024, vol. 175, p. 104662.
9. Premkumar K., Manikandan B. V. Speed control of Brushless DC motor using bat algorithm optimized Adaptive Neuro-Fuzzy Inference System, Applied Soft Computing, 2015, vol. 32, pp. 403—419.
10. Mosavi M. R., Rahmati A., Khoshsaadat A. Design of efficient adaptive neuro-fuzzy controller based on supervisory learning capable for speed and torque control of BLDC motor, Electrical Review, 2012, vol. 88 (1a), pp. 238—246.
11. Megrini M., Gaga A., Mehdaoui Y. Processor in the loop implementation of artificial neural network controller for BLDC motor speed control, Results in Engineering, 2024, vol. 23, p. 102422.
12. Joseph S. B., Dada E. G., Abidemi A., Oyewola D. O., Khammas B. M. Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems, Heliyon, 2022, vol. 8, e09399.
13. Ibrahim M. A., Mahmood A. Kh., Sultan N. S. Optimal PID controller of a brushless DC motor using genetic algorithm, International Journal of Power Electronics and Drive System, 2019, vol. 10, pp. 822—830.
14. Pakdeeto J., Wansungnoen S., Areerak K., Areerak K. Optimal Speed Controller Design of Commercial BLDC Motor by Adaptive Tabu Search Algorithm, IEEE Access, 2023, vol. 11, pp. 79710—79720.
15. Poudel Y. K., Bhandari P. Control of the BLDC Motor Using Ant Colony Optimization Algorithm for Tuning PID Parameters, Archives of Advanced Engineering Science, 2024, vol. 2, no. 2, pp. 108—131.
16. Younus S. M. Y., Kutbay U., Rahebi J., Hardala F. Hybrid Gray Wolf Optimization—Proportional Integral Based Speed Controllers for Brush-Less DC Motor, Energies, 2023, vol. 16, p. 1640.
17. Bora T. C., Coelho L.d.S., Lebensztajn L. Bat-inspired optimization approach for the brushless DC wheel motor problem, IEEE Transactions on Magnetics, 2012, vol. 48, pp. 947—950.
18. Hodashinsky I. A. Methods for improving the efficiency of swarm optimization algorithms. a survey, Automation and Remote Control, 2021, vol. 82, no. 6, pp. 935—967.
19. Ivorra B., Mohammadi B., Ramos A. M. A multi-layer line search method to improve the initialization of optimization algorithms, European Journal of Operational Research, 2015, vol. 247, pp. 711—720.
20. Hodashinsky I. A. Population diversity management of swallow swarm optimization algorithm for fuzzy classification problem, Automatic Documentation and Mathematical Linguistics, 2024, vol. 58, no. 3, pp. 182—187.
21. Huang C., Li Y., Yao X. A Survey of Automatic Parameter Tuning Methods for Metaheuristics, IEEE Transactions on Evolutionary Computation, 2020, vol. 24, pp. 201—216.
22. Wolpert D., Macready W. No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, 1997, vol. 1, pp. 67—82.
23. Tong C., Wang M., Zhao B., Yin Z., Zheng P. A Novel Sensorless Control Strategy for Brushless Direct Current Motor Based on the Estimation of Line Back Electro-Motive Force, Ener gies, 2017, vol. 10, no. 9, p. 1384.
24. Li Y. 3S optimizer: a new meta heuristic global optimization algorithm, Evolutionary Intelligence, 2024, vol. 17, pp. 3535—3552.
Review
For citations:
Mai X.D., Hodashinsky I.A., Shurygin Yu.A. PID-Controller Parameters Optimization of a Brushless DC Motor Using the 3SO Algorithm. Mekhatronika, Avtomatizatsiya, Upravlenie. 2025;26(9):480-487. (In Russ.) https://doi.org/10.17587/mau.26.480-487
Views:
33
.png)
Email this article 