Sliding mode sensorless control model of hub motor based on digital twin technology
-
Bin Hong
,
Fugeng Li
Abstract
This paper introduces a novel integration of sliding mode control and digital twin technology to develop a sensorless control model for hub motors. The proposed approach overcomes the limitations of traditional methods, such as the inability to pre-evaluate parameter changes and the lack of multidimensional monitoring and interaction analysis. The constructed model enables real-time simulation that closely reflects actual motor behavior, allowing precise tuning and validation of control parameters. Simulation results show that the system achieves rapid response, robust performance under varying load and speed conditions, and superior adaptability compared to existing methods. These advancements highlight the potential of this approach for precise and robust motor control in robotics and autonomous vehicles.
-
Research ethics: Not applicable.
-
Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
-
Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Use of Large Language Models, AI and Machine Learning Tools: None declared.
-
Conflict of interests: The authors state no conflict of interest.
-
Research funding: None declared.
-
Data availability: The raw data can be obtained on request from the corresponding author.
References
1. Saghafian, M, Seyedzadeh, H, Moradmand, A. Numerical simulation of electroosmotic flow in a rectangular microchannel with use of magnetic and electric fields. Sci Iran 2024;31:1359–73. https://doi.org/10.24200/sci.2023.58474.5742.Search in Google Scholar
2. Soleimani, A, Hosseini Dolatabadi, S, Heidari, M, Pinnarelli, A, Mehdizadeh Khorrami, B, Luo, Y, et al.. Progress in hydrogen fuel cell vehicles and up-and-coming technologies for eco-friendly transportation: an international assessment. Multiscale and Multidiscip Model Exp Des 2024;7:3153–72. https://doi.org/10.1007/s41939-024-00482-8.Search in Google Scholar
3. Reigosa, D, Fernandez, D, Gonzalez, C, Lee, SB, Briz, F. Permanent magnet synchronous machine drive control using analog hall-effect sensors. IEEE Trans Ind Appl 2018;54:2358–69. https://doi.org/10.1109/tia.2018.2802950.Search in Google Scholar
4. Wang, G, Valla, M, Solsona, J. Position sensorless permanent magnet synchronous machine drives–a review. IEEE Trans Ind Electron 2019;67:5830–42. https://doi.org/10.1109/tie.2019.2955409.Search in Google Scholar
5. Tahami, H, Saberi, S, Ali, BM, AbdulAmeer, S, Abdul Hussein, AH, Chaoui, H. A robust hꝏ-based state feedback control of permanent magnet synchronous motor drives using adaptive fuzzy sliding mode observers. Actuators 2024;13:307. https://doi.org/10.3390/act13080307.Search in Google Scholar
6. Tahami, H, Akbari, E, Mohammed, AH, Faraji, R, Channumsin, S. A transformerless enhanced-boost quasi-Z-source inverter with low input current ripple for stand-alone RES-based systems. Energies 2023;16:2611. https://doi.org/10.3390/en16062611.Search in Google Scholar
7. Attar, A, Bouchnaif, J, Grari, K. Control of Brushless DC motors using sensorless Back-EMF integration method. Mater Today Proc 2021;45:7438–43. https://doi.org/10.1016/j.matpr.2021.01.861.Search in Google Scholar
8. Zossak, S, Sovicka, P, Sumega, M, Rafajdus, P. Evaluating low speed limit of back-EMF observer for permanent magnet synchronous motor. Trans Res Procedia 2019;40:610–15. https://doi.org/10.1016/j.trpro.2019.07.087.Search in Google Scholar
9. Gaballah, MM, El Bardini, M, Sharaf, M. Chattering-free sliding mode observer for speed sensorless control of PMSM. Appl Comput Inform 2017;13:169–74. https://doi.org/10.1016/j.aci.2016.12.002.Search in Google Scholar
10. Alarcon, ML, Roy, U, Kambhampati, A, Nguyen, N, Attari, M, Surya, R, et al.. Learning-based image analytics in user-AI agent interactions for cyber-enabled manufacturing. In: 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS). Toronto, ON, Canada: IEEE; 2024:1–7 pp.10.1109/ICHMS59971.2024.10555867Search in Google Scholar
11. Chang, W, Soma, P, Chen, H, Chang, H, Tsai, C. A hybrid firefly with dynamic multi-swarm particle swarm optimization for WSN deployment. J. Internet Technol. 2023;24:825–36. Retrieved from https://jit.ndhu.edu.tw/article/view/292610.53106/160792642023072404001Search in Google Scholar
12. Wang, X, Xiaopeng Wang, Shu-Chuan Chu, Václav Snášel, HAS, Shehadeh, HA. Five phases algorithm: a novel meta-heuristic algorithm and its application on economic load dispatch problem. J Internet Technol 2023;24:837–48. https://doi.org/10.53106/160792642023072404002. https://jit.ndhu.edu.tw/article/view/2927.10.53106/160792642023072404002Search in Google Scholar
13. Jegadeesan, N, Nirmala Jegadeesan, GB, Balasubramanian, GNH, Hemavathi, NYCC. Empirical validation of fuzzy logic based predictive load scheduling in mimic home energy management system. J Internet Technol 2023;24:1429–36. https://doi.org/10.53106/160792642023122407004. https://jit.ndhu.edu.tw/article/view/2998.10.53106/160792642023122407004Search in Google Scholar
14. Gao, Q, Zong, CS. Rotor position estimation method of PMSM based on recurrent neural network. Energy Rep 2022;8:883–9. https://doi.org/10.1016/j.egyr.2021.11.091.Search in Google Scholar
15. Vrontis, D, Christofi, M, Pereira, V, Tarba, S, Makrides, A, Trichina, E. Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Int J Hum Resour Manag 2022;33:1237–66. https://doi.org/10.1080/09585192.2020.1871398.Search in Google Scholar
16. Qutubuddin, MD, Yadaiah, N. A new intelligent adaptive mechanism for sensorless control of permanent magnet synchronous motor drive. Biol Inspired Cognit Architect 2018;24:47–58. https://doi.org/10.1016/j.bica.2018.04.003.Search in Google Scholar
17. L. Jiao, P. Zhang, R. Min, Y. Luo and D. Yang. Research on PMSM sensorless control based on improved RBF neural network algorithm. In: 2018 37th Chinese control conference (CCC). Wuhan, China: IEEE; 2018.10.23919/ChiCC.2018.8483974Search in Google Scholar
18. Khlaief, A, Boussak, M, Chaari, A. A MRAS-based stator resistance and speed estimation for sensorless vector controlled IPMSM drive. Elec Power Syst Res 2014;108:1–15, 123. https://doi.org/10.1016/j.epsr.2013.09.018.Search in Google Scholar
19. Gaballah, MM, El Bardini, M, Sharaf, M. Chattering-free sliding mode observer for speed sensorless control of PMSM. Appl Comput Inform 2017;13:169–74, 123. https://doi.org/10.1016/j.aci.2016.12.002.Search in Google Scholar
© 2025 Walter de Gruyter GmbH, Berlin/Boston
