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Estimation of PID parameters of BLDC motor system by using machine learning methods

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Abstract

Brushless Direct Current (BLDC) motor control in drones and unmanned aerial vehicles is critical for safety, performance, and high precision. In this study, a method based on machine learning rather than traditional methods is proposed to automatically control a system using a BLDC. An experimental system using a brushless direct current motor used in unmanned aerial vehicles was designed and a data set was created with the control studies. For the obtained data set, the Proportional- Integral- Derivative (PID) values were changed at certain intervals and the error values that occurred when applied to the system were recorded. The PID parameters obtained by seven different machine learning methods and the traditional method are compared. The performances of the machine learning methods were evaluated using regression estimation error metrics. According to the results obtained, Kp, Ki, and Kd values were applied to the system. The system response to sine input and step input is compared. When all machine learning experiments were evaluated, the Stochastic Gradient Descent (SGD) method was the most successful method, achieving 99.988% prediction success according to the R2 metric. When the results are analyzed, it is concluded that the system can be successfully controlled automatically using machine learning techniques.

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Data availability

The data set can be shared if requested to the corresponding author for a reasonable reason.

Abbreviations

TLBO:

Teaching–learning-based optimization algorithm

SVR:

Support vector regression

MLR:

Multi-layer perceptron regressor

SGD:

Stochastic gradient descent

GB:

Gradient boosting regression

RF:

Random forest regressor

KR:

K-Nearest neighbors regressor

DT:

Decision tree regressor

BLDC:

Brushless direct current

UAVs:

Unmanned aerial vehicles

IoT:

İNternet of Things

SAR:

Search and rescue

PSO:

Particle swarm optimization

PV:

Photovoltaic

DC:

Direct current

BBO:

Biogeography-based optimization

GA:

Genetic algorithm

ESC:

Electronic stability control

PWM:

Pulse width modulation

WFA-HBVPID:

Wavelet-based fuzzy adaptive hybrid bat-vulture PID

HHHPSO:

Hybrid horse herd particle swarm optimization

ATLBO:

Fuzzy adaptive teaching learning-based optimization

MSE:

Mean squared error

RMSE:

Root mean squared error

MAE:

Mean absolute error

LVQ:

Learning vector quantization

PI:

Proportional- integral

References

  1. Hafeez, A., et al.: Implementation of drone technology for farm monitoring & pesticide spraying: a review. Inf. Process. Agric. 10(2), 192–203 (2023). https://doi.org/10.1016/J.INPA.2022.02.002

    Article  MATH  Google Scholar 

  2. Mohsan, S.A.H., Khan, M.A., Noor, F., Ullah, I., Alsharif, M.H.: Towards the unmanned aerial vehicles (UAVs): a comprehensive review. Drones 6(6), 147 (2022). https://doi.org/10.3390/DRONES6060147

    Article  MATH  Google Scholar 

  3. Nwaogu, J.M., Yang, Y., Chan, A.P.C., Lin Chi, H.: Application of drones in the architecture, engineering, and construction (AEC) industry. Autom. Constr. 150, 104827 (2023). https://doi.org/10.1016/J.AUTCON.2023.104827

    Article  MATH  Google Scholar 

  4. Mohanraj, D., et al.: A review of BLDC motor: state of art, advanced control techniques, and applications. IEEE Access 10, 54833–54869 (2022). https://doi.org/10.1109/ACCESS.2022.3175011

    Article  MATH  Google Scholar 

  5. Elmeseiry, N., Alshaer, N., Ismail, T.: A detailed survey and future directions of unmanned aerial vehicles (UAVs) with potential applications. Aerospace 8(12), 363 (2021). https://doi.org/10.3390/AEROSPACE8120363

    Article  Google Scholar 

  6. Javaid, S., et al.: Communication and control in collaborative UAVs: recent advances and future trends. IEEE Trans. Intell. Transp. Syst.Intell. Transp. Syst. 24(6), 5719–5739 (2023). https://doi.org/10.1109/TITS.2023.3248841

    Article  MATH  Google Scholar 

  7. Sharma, A., et al.: Communication and networking technologies for UAVs: a survey. J. Netw. Comput. Appl.Netw. Comput. Appl. 168, 102739 (2020). https://doi.org/10.1016/J.JNCA.2020.102739

    Article  MATH  Google Scholar 

  8. Gupta, A., Afrin, T., Scully, E., Yodo, N.: Advances of UAVs toward future transportation: the state-of-the-art, challenges, and opportunities. Future Transportation 1(2), 326–350 (2021). https://doi.org/10.3390/FUTURETRANSP1020019

    Article  Google Scholar 

  9. Peksa, J., Mamchur, D.: A review on the state of the art in copter drones and flight control systems. Sensors 24(11), 3349 (2024). https://doi.org/10.3390/S24113349

    Article  MATH  Google Scholar 

  10. Kanat, Ö.Ö.: The significance of unmanned aerial vehicles (UAVs) in strategic contexts. Anadolu Strateji Dergisi 5(2), 75–87 (2023)

    MATH  Google Scholar 

  11. Musumeci, S., Kroičs, K., Ids, A., Umanis, B.: BLDC motor speed control with digital adaptive PID-fuzzy controller and reduced harmonic content. Energies 17(6), 1311 (2024). https://doi.org/10.3390/EN17061311

    Article  Google Scholar 

  12. Alhayanı, F., Jaber, A.S., Aydın, Ç., Atilla, D.Ç.: Tuning of Pid controller for four-area load frequency control using elephant herding optimization. AURUM J. Eng. Syst. Archit. 3(2), 215–225 (2020)

    MATH  Google Scholar 

  13. Nie, Z.Y., Li, Z., Wang, Q.G., Gao, Z., Luo, J.: A unifying Ziegler-Nichols tuning method based on active disturbance rejection. Int. J. Robust Nonlinear Control 32(18), 9525–9541 (2022). https://doi.org/10.1002/RNC.5848

    Article  MathSciNet  MATH  Google Scholar 

  14. Muresan, C.I., De Keyser, R.: Revisiting Ziegler-Nichols. a fractional order approach. ISA Trans. 129, 287–296 (2022). https://doi.org/10.1016/J.ISATRA.2022.01.017

    Article  MATH  Google Scholar 

  15. Patel, V.V.: Ziegler-Nichols tuning method: understanding the PID controller. Resonance 25(10), 1385–1397 (2020). https://doi.org/10.1007/S12045-020-1058-Z/METRICS

    Article  MATH  Google Scholar 

  16. Borase, R.P., Maghade, D.K., Sondkar, S.Y., Pawar, S.N.: A review of PID control, tuning methods and applications. Int. J. Dyn. Control 9(2), 818–827 (2021). https://doi.org/10.1007/S40435-020-00665-4/FIGURES/1

    Article  MathSciNet  Google Scholar 

  17. Yilmaz, G.: Comparison of different methods for optimization of PID controller gain coefficients. Kırklareli Üniversitesi Mühendislik ve Fen Bilimleri Dergisi 9(2), 254–264 (2023). https://doi.org/10.34186/KLUJES.1310728

    Article  MathSciNet  MATH  Google Scholar 

  18. Bigazzi, L., Gherardini, S., Innocenti, G., Basso, M.: Development of non expensive technologies for precise maneuvering of completely autonomous unmanned aerial vehicles. Sensors (Basel) 21(2), 1–24 (2021). https://doi.org/10.3390/S21020391

    Article  Google Scholar 

  19. Telli, K., et al.: A comprehensive review of recent research trends on unmanned aerial vehicles (UAVs). Systems 11(8), 400 (2023). https://doi.org/10.3390/SYSTEMS11080400

    Article  MATH  Google Scholar 

  20. Rejeb, A., Rejeb, K., Simske, S.J., Treiblmaier, H.: Drones for supply chain management and logistics: a review and research agenda. Int J Log Res Appl 26(6), 708–731 (2023). https://doi.org/10.1080/13675567.2021.1981273

    Article  MATH  Google Scholar 

  21. Kapustina, L., Izakova, N., Makovkina, E., Khmelkov, M.: The global drone market: main development trends. SHS Web of Conferences 129, 11004 (2021). https://doi.org/10.1051/SHSCONF/202112911004

    Article  MATH  Google Scholar 

  22. Mohsan, S.A.H., Othman, N.Q.H., Li, Y., Alsharif, M.H., Khan, M.A.: Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. Intell. Service Robot. 16(1), 109–137 (2023). https://doi.org/10.1007/S11370-022-00452-4

    Article  Google Scholar 

  23. Lyu, M., Zhao, Y., Huang, C., Huang, H.: Unmanned aerial vehicles for search and rescue: a survey. Rem. Sens. 15(13), 3266 (2023). https://doi.org/10.3390/RS15133266

    Article  MATH  Google Scholar 

  24. Carev, V., Roháč, J., Šipoš, M., Schmirler, M.: A multilayer brushless DC motor for heavy lift drones. Energies 14(9), 2504 (2021). https://doi.org/10.3390/EN14092504

    Article  Google Scholar 

  25. Gamazo-Real, J.C., Vázquez-Sánchez, E., Gómez-Gil, J.: Position and speed control of brushless DC motors using sensorless techniques and application trends. Sensors 10(7), 6901–6947 (2010). https://doi.org/10.3390/S100706901

    Article  MATH  Google Scholar 

  26. Çabuk, A.S.: Sensorless control of outer rotor brushless DC motor with back-EMF observer for drone. Balkan J. Electric. Comput. Eng. 9(4), 379–385 (2021). https://doi.org/10.17694/BAJECE.958760

    Article  MATH  Google Scholar 

  27. Osmani, K., Schulz, D.: Comprehensive ınvestigation of unmanned aerial vehicles (UAVs): an ın-depth analysis of avionics systems. Sensors 24(10), 3064 (2024). https://doi.org/10.3390/S24103064

    Article  MATH  Google Scholar 

  28. Imran, I.H., Wood, K., Montazeri, A.: Adaptive control of unmanned aerial vehicles with varying payload and full parametric uncertainties. Electronics 13(2), 347 (2024). https://doi.org/10.3390/ELECTRONICS13020347

    Article  MATH  Google Scholar 

  29. Pereira, F.L.: Optimal control problems in drone operations for disaster search and rescue. Procedia Comput Sci 186, 78–86 (2021). https://doi.org/10.1016/J.PROCS.2021.04.127

    Article  MATH  Google Scholar 

  30. Maghfiroh, H., Wahyunggoro, O., Cahyadi, A. I., Praptodiyono, S.: “PID-hybrid tuning to improve control performance in speed control f DC motor base on PLC. In: Proceedings of 2013 3rd international conference on ınstrumentation, control and automation, ICA 2013, pp. 233–238, 2013, https://doi.org/10.1109/ICA.2013.6734078.

  31. Maghfiroh, H., Saputro, J.S., Hermanu, C., Ibrahim, M.H., Sujono, A.: Performance evaluation of different objective function in PID tuned by PSO in DC-motor speed control. IOP Conf. Ser. Mater. Sci. Eng. 1096(1), 012061 (2021). https://doi.org/10.1088/1757-899X/1096/1/012061

    Article  Google Scholar 

  32. Lins, A.W., Krishnakumar, R.: Tuning of PID controller for a PV-fed BLDC motor using PSO and TLBO algorithm. Appl. Nanosci. (Switzerland) 13(4), 2911–2934 (2023). https://doi.org/10.1007/S13204-021-02272-X/FIGURES/36

    Article  MATH  Google Scholar 

  33. Anshory, I., et al.: Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm. Results in Engineering 21, 101727 (2024). https://doi.org/10.1016/J.RINENG.2023.101727

    Article  MATH  Google Scholar 

  34. Kumar, K., Singh Yadav, A., Yadav, A., Mehdi, A., Pal, A., Fouad, L.: Speed analysis of BLDC motor by ımplementation of fuzzy logic based PID controller. In: 2023 3rd International conference on advance computing and ınnovative technologies in engineering, ICACITE 2023, pp. 1980–1985, 2023, https://doi.org/10.1109/ICACITE57410.2023.10182807.

  35. Abdolhosseini, M., Abdollahi, R.: Performance analysis of PID controller-based metaheuristic optimisation algorithms for BLDC motor. Aust. J. Electr. Electron. Eng.Electr. Electron. Eng. 20(4), 400–411 (2023). https://doi.org/10.1080/1448837X.2023.2249205

    Article  MATH  Google Scholar 

  36. Bhandari, P., et al.: Application of particle swarm optimization (PSO) algorithm for PID parameter tuning in speed control of brushless DC (BLDC) motor. J. Phys. Conf. Ser. 2570(1), 012018 (2023). https://doi.org/10.1088/1742-6596/2570/1/012018

    Article  MATH  Google Scholar 

  37. Guntay, S., Saritas, I.: BLDC motor speed control with dynamic adjustment of PID coefficients: comparison of fuzzy and classic PID. Int. J. Appl. Methods in Electron. Comput. 12(1), 22–32 (2024). https://doi.org/10.58190/IJAMEC.2023.80

    Article  MATH  Google Scholar 

  38. Kanungo, A., Choubey, C., Gupta, V., Kumar, P., Kumar, N.: Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motor. Multimed. Tools Appl. 82(21), 33203–33223 (2023). https://doi.org/10.1007/S11042-023-14872-6/TABLES/5

    Article  MATH  Google Scholar 

  39. RamaKrishnan, A., Shunmugalatha, A., Premkumar, K.: An improved tuning of PID controller for PV battery-powered brushless DC motor speed regulation using hybrid horse herd particle swarm optimization. Int. J. PhotoenergyPhotoenergy 2023(1), 2777505 (2023). https://doi.org/10.1155/2023/2777505

    Article  Google Scholar 

  40. Kumar, R., Bera, C., Kumar, A.: Optimization of BLDC-based electric vehicles: vehicle dynamics modelling through dual-motor approach and designing a novel augmented TLBO algorithm for PID control. Eng. Res. Exp. 6(2), 025334 (2024). https://doi.org/10.1088/2631-8695/AD45B3

    Article  MATH  Google Scholar 

  41. Arikusu, Y.S., Bayhan, N.: Design of a novel PID controller based on machine learning algorithm for a micro-thermoelectric cooler of the polymerase chain reaction device. IEEE Access 12, 61959–61977 (2024). https://doi.org/10.1109/ACCESS.2024.3392734

    Article  MATH  Google Scholar 

  42. Tibor, B., Fedak, V., Ďurovský, F.: Modeling and simulation of the BLDC motor in MATLAB GUI. In: Proceedings - ISIE 2011: 2011 IEEE International symposium on ındustrial electronics, pp. 1403–1407, (2011), https://doi.org/10.1109/ISIE.2011.5984365.

  43. Lopez-Sanchez, I., Moreno-Valenzuela, J.: PID control of quadrotor UAVs: a survey. Annu. Rev. Control.. Rev. Control. 56, 100900 (2023). https://doi.org/10.1016/J.ARCONTROL.2023.100900

    Article  MathSciNet  MATH  Google Scholar 

  44. Guzmán, J.L., Hägglund, T.: Tuning rules for feedforward control from measurable disturbances combined with PID control: a review. Int. J. Control. 97(1), 2–15 (2024). https://doi.org/10.1080/00207179.2021.1978537

    Article  MathSciNet  MATH  Google Scholar 

  45. Abdelghany, M.A., Elnady, A.O., Ibrahim, S.O.: Optimum PID controller with fuzzy self-tuning for DC servo motor. J. Robot. Control (JRC) 4(4), 500–508 (2023). https://doi.org/10.18196/JRC.V4I4.18676

    Article  Google Scholar 

  46. Alkhatib, R., Sahwan, W., Alkhatieb, A., Schütt, B.: A brief review of machine learning algorithms in forest fires science. Appl. Sci. 13(14), 8275 (2023). https://doi.org/10.3390/APP13148275

    Article  MATH  Google Scholar 

  47. Ighalo, J.O., Adeniyi, A.G., Marques, G.: Application of linear regression algorithm and stochastic gradient descent in a machine-learning environment for predicting biomass higher heating value. Biofuels Bioprod. Biorefin.Bioprod. Biorefin. 14(6), 1286–1295 (2020). https://doi.org/10.1002/BBB.2140

    Article  Google Scholar 

  48. Pekel, E.: Estimation of soil moisture using decision tree regression. Theor. Appl. Climatol.. Appl. Climatol. 139(3–4), 1111–1119 (2020). https://doi.org/10.1007/S00704-019-03048-8/FIGURES/8

    Article  MATH  Google Scholar 

  49. Wang, Y., Fang, Z., Hong, H., Costache, R., Tang, X.: Flood susceptibility mapping by integrating frequency ratio and index of entropy with multilayer perceptron and classification and regression tree. J. Environ. Manage. 289, 112449 (2021). https://doi.org/10.1016/J.JENVMAN.2021.112449

    Article  Google Scholar 

  50. Wang, F., Wang, Y., Zhang, K., Hu, M., Weng, Q., Zhang, H.: Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation. Environ. Res. 202, 111660 (2021). https://doi.org/10.1016/J.ENVRES.2021.111660

    Article  MATH  Google Scholar 

  51. Ghunimat, D., Alzoubi, A.E., Alzboon, A., Hanandeh, S.: Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression. Asian J. Civil Eng. 24(1), 169–177 (2023). https://doi.org/10.1007/S42107-022-00495-Z/FIGURES/6

    Article  Google Scholar 

  52. Otchere, D.A., Ganat, T.O.A., Ojero, J.O., Tackie-Otoo, B.N., Taki, M.Y.: Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. J. Pet. Sci. Eng. 208, 109244 (2022). https://doi.org/10.1016/J.PETROL.2021.109244

    Article  Google Scholar 

  53. Lin, G., Lin, A., Gu, D.: Using support vector regression and K-nearest neighbors for short-term traffic flow prediction based on maximal information coefficient. Inf Sci (N Y) 608, 517–531 (2022). https://doi.org/10.1016/J.INS.2022.06.090

    Article  MATH  Google Scholar 

  54. Serefoglu Cabuk, K., et al.: Chasing the objective upper eyelid symmetry formula; R2 RMSE, POC, MAE, and MSE. Int. Ophthalmol. 44(1), 1–9 (2024). https://doi.org/10.1007/S10792-024-03157-Y/TABLES/3

    Article  Google Scholar 

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GT: wrote the main manuscript text. GT: Project administration and supervision. MO: experimental system applications. GT: investigation, conceptualization. All authors reviewed the manuscript.

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Correspondence to Göksu Taş.

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Taş, G., Özdamar, M. Estimation of PID parameters of BLDC motor system by using machine learning methods. SIViP 19, 140 (2025). https://doi.org/10.1007/s11760-024-03714-z

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