An Optimized PID Controller Desing for BLDC Motor Using Nature-Inspired Algorithms

Öz

For the optimal control of speed in a brushless DC motor, it is crucial to appropriately adjust the parameters of the PID controller. This study addresses the determination of PID controller parameters using nature-inspired metaheuristic optimization algorithms. Initially, the dynamic model of the brushless DC motor is formulated in the MATLAB/Simulink environment. The grey wolf optimization algorithm, whale optimization algorithm, and firefly algorithm are successively applied to the simulation model to optimize the PID controller parameters. The integral time absolute error objective function is utilized to compare the error performances of these algorithms. Additionally, performance evaluations are conducted concerning parameters such as rise time, settling time, and maximum overshoot. As a result of the comparison based on the fitness criteria, it was determined that the grey wolf optimization algorithm is 35% more successful than the algorithm that provided the next closest result.

Anahtar Kelimeler

• Brushless DC motor
• Nature-inspired algorithms
• PID control

Kaynakça

1. Abdolhosseini M, Abdollahi, R. 2023. Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor. Australian J Elect Electron Engin, 20(4): 400–411.
2. Águila-León J, Chiñas-Palacios CD, Vargas-salgado C, Hurtado-perez E, García, EXM. 2020. Optimal PID parameters tunning for a DC-DC boost converter: a performance comparative using grey wolf optimizer, particle swarm optimization and genetic algorithms. In 2020 IEEE Conference on Technologies for Sustainability (SusTech), Santa Ana, CA, USA, pp: 1–6.
3. Aguilar-Mejía O, Minor-Popocatl H, Tapia-Olvera R. 2020. Comparison and ranking of metaheuristic techniques for optimization of PI controllers in a machine drive system. Applied Sci, 10(18): 6592.
4. Azari MN, Samami M, Pahnehkolaei SMA. 2017. Optimal design of a brushless DC motor , by cuckoo optimization algorithm. Inter J Engin, 30(5): 668–677.
5. Bober P. 2017. Measurement of objective function for BLDC motor optimization. Acta Electrotech Inform, 17(4): 43–49.
6. Çetintaş OG, Akgül K, Ergene LT. 2023. Position Sensorless speed control of BLDC motor with using back-EMF method. In 2023 14th Inter Conference on Electrical and Electronics Engin (ELECO), pp: 1–6.
7. Ch L, Palakeerthi R. 2015. BLDC drive control using artificial intelligence technique. Inter J Computer Applicat, 118(4): 5–9.
8. Chittajallu T, Lanka, RS. 2023. An effective controller design for BLDC motor drive with nature inspired heuristic algorithm. In: International Conference on Artificial Intelligence Techniques for Electrical Engin Systems, pp: 268–280.

9. Demir BE, Demir F. 2023. Comparison of metaheuristic optimization algorithms for quadrotor PID controllers. Tehnički Vjesnik, 30(4): 1096–1103.
10. Ehsani M, Singh KV, Bansal HO, Mehrjardi RT. 2021. State of the Art and trends in electric and hybrid electric vehicles. Proceed IEEE, 109(6): 967–984.
11. Joseph SB, Dada EG, Abidemi A, Oyewola DO, Khammas BM. 2022. Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems. Heliyon, 8(5): e09399.
12. Jun S, Qingtao M, Weifeng C, Lintao Z. 2022. Optimizing BLDC motor drive performance using particle swarm algorithm‑tuned fuzzy logic controller. SN Applied Sci, 4(293).
13. Khubalkar SW, Chopade AS, Junghare SA, Aware MV. 2016. Design and tuning of fractional order PID controller for speed control of permanent magnet brushless DC motor. In: 2016 IEEE First Inter Conference on Control, Measurement and Instrumentation (CMI), pp: 320–326.
14. Kumar V, Kumar D. 2021. A systematic review on firefly algorithm: past, present, and future. Archiv Computat Methods Engin, 28(4): 3269–3291.
15. Mahmud M, Motakabber SMA, Alam AHMZ, Nordin AN. 2020. Control BLDC motor speed using PID controller. Inter J Adv Comput Sci Applicat, 11(3): 477–481.
16. Mirjalili S, Lewis A. 2016. The whale optimization algorithm. Adv Engin Software, 95: 51–67.
17. Mirjalili S, Mirjalili SM, Lewis A. 2014. Grey wolf optimizer. Adv Engin Software, 69: 46–61.
18. Mondal S, Mitra A, Chattopadhyay M. 2015. Mathematical modeling and simulation of brushless DC motor with ideal back EMF for a precision speed control. In: 2015 IEEE Inter Conference on Electrical, Computer and Communication Technologies (ICECCT), pp: 1–5.
19. Nisi K, Nagaraj B, Agalya A. 2019. Tuning of a PID controller using evolutionary multi objective optimization methodologies and application to the pulp and paper industry. Inter J Machine Learn Cybernet, 10: 2015–2025.
20. Potnuru D, Ayyarao TSLV, Kumar LVS, Kumar YVP, Pradeep DJ, Reddy CP. 2022. Salp swarm algorithm based optimal speed control for electric vehicles. Inter J Power Electron Drive Systems, 13(2): 755–763.
21. Praptodiyono S, Maghfiroh H, Hermanu C. 2020. BLDC motor control optimization using optimal adaptive PI algorithm. J Elektron Dan Telekom, 20(2): 47–52.
22. Premkumar M, Sowmya R, Jangir P, Nisar KS, Aldhaifallah M. 2021. A new metaheuristic optimization algorithms for brushless direct current wheel motor design problem. Comput, Mater Continua, 62(2): 2227-2242.
23. Santra SB, Chatterjee A, Chatterjee D, Padmanaban S, Bhattacharya K. 2022. High efficiency operation of brushless DC motor drive using optimized harmonic minimization based switching technique. IEEE Transact Indust Applicat, 58(2): 2122–2133.
24. Temir A, Durmuş B. 2023. Equilibrium optimizer based fractional order PID control of brushless DC motor. European J Sci Technol, 51: 153–161.