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.
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig1_HTML.jpg)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig2_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig3_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig4_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig5_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig6_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig7_HTML.png)
![](http://media.springernature.com/m312/springer-static/image/art%3A10.1007%2Fs11760-024-03714-z/MediaObjects/11760_2024_3714_Fig8_HTML.png)
Similar content being viewed by others
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
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
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
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
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
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
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
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
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
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
Kanat, Ö.Ö.: The significance of unmanned aerial vehicles (UAVs) in strategic contexts. Anadolu Strateji Dergisi 5(2), 75–87 (2023)
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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.
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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
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
Funding
There is no funding for this research.
Author information
Authors and Affiliations
Contributions
GT: wrote the main manuscript text. GT: Project administration and supervision. MO: experimental system applications. GT: investigation, conceptualization. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Human and animal participants
There is no human or animal participation in this research.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
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
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11760-024-03714-z