Abstract
The most common actuators in precision or low-power applications are Direct Current (DC) motors. DC motors are in robots, commercial products, automobiles, and appliances for accurately controlling position or speed or in portable devices. Input tracking and system stabilization are the main goals of control theory. There are different approaches for classic control with their proprieties. However, classical controllers and sometimes others with probabilistic methods use mathematical models for designing or generating control laws. Alternatives that represent the model of a system include differential and difference equations, state space models, and transfer functions. Parametrizing those models implies supplying signals to the process and using deterministic or probabilistic algorithms. On the other hand, artificial intelligence and machine learning approaches like genetic algorithms have shown relevant results by tuning those models using the error with the system’s frequency or time response. However, looking for reasonable solutions in search spaces with several dimensions, like in a five-dimensional problem optimizing the parameters of a DC motor, can be timely and computationally demanding. Alternatively, few-shots learning simplifies the data for optimization, resulting in less timely and computational training. In this work, we propose a novel method for transfer function DC motor identification using a few-shots learning approach based on a signature value unique for each motor. We obtained the motor signature by applying a signal that variates length and frequency for each engine and collecting the six numerical values with the exact time sampling. Since we reduce the components of the signals, we reduce the data size. Moreover, the search space becomes significantly simple, allowing us to find the DC motor transfer functions with R-square between 0.99 and 1.0 for at least 95% of the controllers with 6 component signatures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sra, I.K., Sra, J.S.: Position control of DC motor by using PID, FLC, ANN controller techniques and the comparison of performances. Int. J. Eng. Res. Technol. 4 (2018). https://doi.org/10.17577/IJERTCONV4IS15018
Rahmatullah, R., Ak, A., Serteller, N.F.O.: SMC controller design for DC motor speed control applications and performance comparison with FLC, PID and PI controllers. In: Nagar, A.K., Singh Jat, D., Mishra, D.K., Joshi, A. (eds.) Intelligent Sustainable Systems, vol. 579, pp. 607–617. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-7663-6_57
Suman, S.K., Giri, V.K.: Speed control of DC motor using optimization techniques based PID controller. In: Proceedings of 2nd IEEE International Conference on Engineering and Technology, ICETECH 2016, pp. 581–587 (2016). https://doi.org/10.1109/ICETECH.2016.7569318
Semenov, A.S., Khubieva, V.M., Kharitonov, Y.S.: Mathematical modeling of static and dynamic modes DC motors in software package MATLAB. In: 2018 International Russian Automation Conference, RusAutoCon 2018 (2018). https://doi.org/10.1109/RUSAUTOCON.2018.8501666
Poovizhi, M., Senthil Kumaran, M., Ragul, P., et al.: Investigation of mathematical modelling of brushless dc motor (BLDC) drives by using MATLAB-SIMULINK. In: International Conference on Power and Embedded Drive Control, ICPEDC 2017, pp. 178–183 (2017). https://doi.org/10.1109/ICPEDC.2017.8081083
Luengo, D., Martino, L., Bugallo, M., et al.: A survey of Monte Carlo methods for parameter estimation. EURASIP J. Adv. Sig. Process. 2020, 2020:1–2020:62 (2020). https://doi.org/10.1186/S13634-020-00675-6
Timofeev, V.S., Shchekoldin, V.Y., Timofeeva, A.Y.: Identification methods for linear regression based on data of household budget surveys. In: 2018 14th International Scientific-Technical Conference on Actual Problems of Electronic Instrument Engineering, APEIE 2018 – Proceedings, pp. 311–314 (2018). https://doi.org/10.1109/APEIE.2018.8545726
Rojas, J., Eichhorn, M., Baatar, G., et al.: Parameter identification and optimization of an oceanographic monitoring remotely operated vehicle. In: 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans, OCEANS - Kobe 2018 (2018). https://doi.org/10.1109/OCEANSKOBE.2018.8559202
Gyuk, P., Vassanyi, I., Kosa, I.: Blood glucose level prediction with improved parameter identification methods. In: IEEE 30th Jubilee Neumann Colloquium, NC 2017, pp. 85–88 (2018). https://doi.org/10.1109/NC.2017.8263257
Obeidat, M.A., Wang, L.Y., Lin, F.: Real-time parameter estimation of PMDC motors using quantized sensors. IEEE Trans. Veh. Technol. 62, 2977–2986 (2013). https://doi.org/10.1109/TVT.2013.2251431
Lian, C., Xiao, F., Gao, S., Liu, J.: Load torque and moment of inertia identification for permanent magnet synchronous motor drives based on sliding mode observer. IEEE Trans. Power Electron. 34, 5675–5683 (2019). https://doi.org/10.1109/TPEL.2018.2870078
Rubaai, A., Kotaru, R.: Online identification and control of a dc motor using learning adaptation of neural networks. IEEE Trans. Ind. Appl. 36, 935–942 (2000). https://doi.org/10.1109/28.845075
Cosmin, B., Sorin, T.: Reinforcement learning for a continuous DC motor controller. In: 15th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2023 - Proceedings (2023). https://doi.org/10.1109/ECAI58194.2023.10193912
Han, L., Fu, X., Tian, S., Shen, Z.: Demagnetization fault detection research of brushless DC motor based on machine learning. In: 2022 5th World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2022, pp. 634–638 (2022). https://doi.org/10.1109/WCMEIM56910.2022.10021516
Bujgoi, G., Sendrescu, D.: DC motor control based on integral reinforcement learning. In: 2022 23rd International Carpathian Control Conference, ICCC 2022, pp. 282–286 (2022). https://doi.org/10.1109/ICCC54292.2022.9805935
Li, H.G., Liu, B.: Knowledge extraction: a few-shot relation learning approach. In: Proceedings - 2022 International Conference on Machine Learning and Knowledge Engineering, MLKE 2022, pp. 261–265 (2022). https://doi.org/10.1109/MLKE55170.2022.00057
Ma, L., Shi, F., Wu, Z., Peng, K.: A survey of few-shot learning-based compound fault diagnosis methods for industrial processes. In: Proceedings - 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems, ICPS 2023 (2023). https://doi.org/10.1109/ICPS58381.2023.10128105
Khatri, K.C.A., Shah, K.B., Logeshwaran, J., Shrestha, A.: Genetic algorithm based techno-economic optimization of an isolated hybrid energy system. ICTACT J. Microelectron. 4 (2023). https://doi.org/10.21917/ijme.2023.0249
Dey, N.: Applied Genetic Algorithm and Its Variants: Case Studies and New Developments. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-3428-7
Aziz, R.M., Mahto, R., Goel, K., et al.: Modified genetic algorithm with deep learning for fraud transactions of ethereum smart contract. Appl. Sci. 13, 697 (2023). https://doi.org/10.3390/APP13020697
Jebari, K., Madiafi, M.: Selecztion methods for genetic algorithms. Int. J. Emerg. Sci. 3, 333–344 (2013)
Alzanin, S.M., Gumaei, A., Haque, M.A., Muaad, A.Y.: An optimized Arabic multilabel text classification approach using genetic algorithm and ensemble learning. Appl. Sci. 13, 10264 (2023). https://doi.org/10.3390/APP131810264
Altarabichi, M.G., Pashami, S., Nowaczyk, S., Mashhadi, P.S.: Fast genetic algorithm for feature selection - a qualitative approximation approach. In: GECCO 2023 Companion - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, pp. 11–12 (2023). https://doi.org/10.1145/3583133.3595823
Kieszek, R., Kachel, S., Kozakiewicz, A.: Modification of genetic algorithm based on extinction events and migration. Appl. Sci. 13, 5584 (2023). https://doi.org/10.3390/APP13095584
Ali, W., Saeed, F.: Hybrid filter and genetic algorithm-based feature selection for improving cancer classification in high-dimensional microarray data. Processes 11, 562 (2023). https://doi.org/10.3390/PR11020562
Zeng, D., Yan, T., Zeng, Z., et al.: A hyperparameter adaptive genetic algorithm based on DQN. J. Circ. Syst. Comput. 32 (2022). https://doi.org/10.1142/S0218126623500627
Greenstein, B.L., Elsey, D.C., Hutchison, G.R.: Best practices for using genetic algorithms in molecular discovery (2023). https://doi.org/10.26434/CHEMRXIV-2023-67HFC
Gen, M., Lin, L.: Genetic algorithms and their applications. In: Pham, H. (ed.) Springer Handbook of Engineering Statistics, pp. 635–374. Springer, London (2023). https://doi.org/10.1007/978-1-4471-7503-2_33
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Montes Rivera, M., Aguilar-Justo, M., Perez Hernández, M. (2024). Identifying DC Motor Transfer Function with Few-Shots Learning and a Genetic Algorithm Using Proposed Signal-Signature. In: Calvo, H., Martínez-Villaseñor, L., Ponce, H., Zatarain Cabada, R., Montes Rivera, M., Mezura-Montes, E. (eds) Advances in Computational Intelligence. MICAI 2023 International Workshops. MICAI 2023. Lecture Notes in Computer Science(), vol 14502. Springer, Cham. https://doi.org/10.1007/978-3-031-51940-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-51940-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-51939-0
Online ISBN: 978-3-031-51940-6
eBook Packages: Computer ScienceComputer Science (R0)