Abstract
In industrial IoT, the design of control algorithms is pivotal to industrial servo systems. Unlike existing work, we study the industrial servo system control through edge computing with semi-closed-loop feedbacks, high-order nonlinear disturbances and lightweight implementation requirements. Particularly, in this paper, we take permanent magnet synchronous motor (PMSM) driven servo system as a typical industrial IoT device, propose a novel deep reinforcement learning based semi-closed-loop control algorithm, i.e., DRL-SCLC, and successfully deploy it on an edge server to provide real-time edge intelligent decision making. The control problem is formulated as a Markov decision process and then solved by the designed DRL-based algorithm, for minimizing the absolute error between reference signal and corresponding system response. To guarantee robustness, we further integrate “three-loop control structure” in traditions to DRL-SCLC for restricting outputs within a desired limit. Experiments on a real-world aerospace servo testbed show that the proposed solution is not only effective but also superior over counterparts.
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
Similar content being viewed by others
Notes
- 1.
Different from terminal feedbacks, PMSM feedbacks can be naturally collected by hall sensors inside PMSM.
References
Chen, J., Yi, C., et al.: Learning aided joint sensor activation and mobile charging vehicle scheduling for energy-efficient wrsn-based industrial iot. IEEE Trans. Veh. Technol. 72(4), 5064–5078 (2023). https://doi.org/10.1109/TVT.2022.3224443
Chen, J., Yi, C., et al.: Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey. IEEE Commun. Surv. Tutor. 26(1), 706–746 (2024). https://doi.org/10.1109/COMST.2023.3308717
Lu, W., Li, Q., Lu, K., Lu, Y., Guo, L., Yan, W., Xu, F.: Load adaptive pmsm drive system based on an improved adrc for manipulator joint. IEEE Access 9, 33369–33384 (2021). https://doi.org/10.1109/ACCESS.2021.3060925
Mohd Zaihidee, F., Mekhilef, S., Mubin, M.: Robust speed control of pmsm using sliding mode control (smc)-a review. Energies 12(9), 1669 (2019)
Shi, Y., Yang, Y., Yi, C., et al.: Towards online reliability-enhanced microservice deployment with layer sharing in edge computing. IEEE Internet Things J., 1 (2024). https://doi.org/10.1109/JIOT.2024.3385816
Verkroost, L., Vandenabeele, T., Sergeant, P., Vansompel, H.: Multi-agent position estimation in modular motor drives using low-resolution sensors. IEEE open j. Ind. Electron. 3, 105–115 (2022). https://doi.org/10.1109/OJIES.2022.3146302
Viswanadhapalli, J.K., Elumalai, V.K., Shivram, S., Shah, S., Mahajan, D.: Deep reinforcement learning with reward shaping for tracking control and vibration suppression of flexible link manipulator. Appl. Soft Comput. 152, 110756 (2024)
Wang, H., Xu, S., Hu, H.: Pid controller for pmsm speed control based on improved quantum genetic algorithm optimization. IEEE Access 11, 61091–61102 (2023). https://doi.org/10.1109/ACCESS.2023.3284971
Yang, C., Song, B., Xie, Y., Zheng, S., Tang, X.: Adaptive identification of nonlinear friction and load torque for pmsm drives via a parallel-observer-based network with model compensation. IEEE Trans. Power Electron. 38(5), 5875–5897 (2023). https://doi.org/10.1109/TPEL.2023.3239609
Yang, Y., Shi, Y., Yi, C., et al.: Dynamic human digital twin deployment at the edge for task execution: A two-timescale accuracy-aware online optimization. arXiv preprint arXiv:2401.16710 (2024)
Acknowledgements
This work was supported by the Postgraduate Research & Practice Innovation Program of NUAA under grant No. xcxjh20231601.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, H., Zhu, H., Wu, H., Yi, C., Zhu, K., Dai, X. (2025). A DRL-Based Edge Intelligent Servo Control with Semi-closed-Loop Feedbacks in Industrial IoT. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14998. Springer, Cham. https://doi.org/10.1007/978-3-031-71467-2_33
Download citation
DOI: https://doi.org/10.1007/978-3-031-71467-2_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-71466-5
Online ISBN: 978-3-031-71467-2
eBook Packages: Computer ScienceComputer Science (R0)