Advancements in Non-contact Force Perturbation Sensing within Magnetic-levitation Control Loops
Keywords:
Magnetic Levitation, Force Perturbation, Disturbance Observers, Non-contact SensingAbstract
The precise regulation of magnetic-levitation systems is inherently dependent on the accurate estimation and rejection of external force perturbations. As these systems operate without physical contact, traditional contact-based force and torque sensors cannot be deployed, necessitating the development of advanced non-contact sensing methodologies integrated directly into the control loop. This paper provides a comprehensive investigation into the mechanisms, algorithms, and practical implementations of non-contact force perturbation sensing. By synthesizing principles from electromagnetic theory and modern control engineering, the research details how variations in the magnetic air gap and coil currents can be transformed into reliable estimates of external disturbances. The analysis explores the limitations of standard position-derivative feedback and advocates for the implementation of advanced observer-based architectures, specifically focusing on extended state observers and disturbance observers. Extensive experimental and analytical discussions elucidate the impact of sensor noise, unmodeled system dynamics, and environmental variables on sensing fidelity. Through a robust methodological framework and detailed comparative analysis, this study demonstrates that integrated algorithmic sensing can achieve disturbance rejection capabilities comparable to physical sensors, thereby enabling high-precision positioning in vibration-sensitive applications such as semiconductor manufacturing and precision aerospace instrumentation.Downloads
References
Li, X., Wang, P., Li, G., & Zhang, Y. (2023). Design of a novel self-test-on-chip interface ASIC for capacitive accelerometers. IEEE Transactions on Circuits and Systems I: Regular Papers, 70(7), 2834-2843.
Wang, S., Zhang, X., Wang, P., & Li, X. (2026). Design of Magnetic Sensor Array-Based PUFs for IoT Security. IEEE Transactions on Instrumentation and Measurement, 75, 1-9.
Yang, Y., Robertson, W. S., Jafari, A., & Arjomandi, M. (2025). Non-contact measurement technique for horizontal force perturbations in magnetic levitation systems. Measurement Science and Technology, 36(4), 046003.
Levine, S., & Koltun, V. (2013). Guided policy search. In Proceedings of ICML.
Zhang, H., Lu, H., Cheng, L., & Wu, H. (2026, May). Verifying an Elevator Scheduling Control System. In 2026 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 129-138). IEEE.
Yang, Y., Jafari, A., Robertson, W., & Arjomandi, M. (2024, December). A novel technical solution for using the magnetic levitation technique to measure skin friction drag. In 24th Australasian Fluid Mechanics Conference.
Yang, Y., Hu, H., Li, W., Li, S., Yang, J., Zhao, Q., & Zhang, C. (2023, June). Flow to control: Offline reinforcement learning with lossless primitive discovery. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 9, pp. 10843-10851).
Lyu, S., Mao, S., & Ma, Y. (2025). Unified Long Video Inpainting and Outpainting via Overlapping High-Order Co-Denoising. arXiv preprint arXiv:2511.03272.
Yu A, Pang F, Yuan Y, et al. Simultaneous current and vibration measurement based on interferometric fiber optic sensor[J]. Optics & Laser Technology, 2023, 161: 109223.
Li, X., Yin, L., Chen, W., Liu, X., & Fu, Q. (2016). Study of a three-axis digital tunneling resistance-type magnetic sensor.IEICE Electronics Express,13(13), 20160319-20160319.
Zhao, J., Zhang, C., Wang, C., & Yang, P. (2025). Learning from expert factors: Trajectory-level reward shaping for formulaic alpha mining. arXiv preprint arXiv:2507.20263.
Xiao, W., Tan, Q., Chen, M., Yao, Y., & Shu, F. (2026). Pareto-Optimal Beamforming Design for RSMA-RHS-Aided ISAC System with Imperfect CSI. IEEE Transactions on Vehicular Technology.
Zhao, Y., Chen, H., Liu, C., Li, Z., Herrmann, C., Hur, J., ... & Xu, M. (2025). Masiv: Toward material-agnostic system identification from videos. arXiv preprint arXiv:2508.01112.
Li, B., Zhang, R., Liang, H., Zhang, J., Zhang, J., Chen, X., ... & Wang, J. (2026). Interagent: Physics-based multi-agent command execution via diffusion on interaction graphs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 15253-15265).
Ouyang, Z., Feng, H., Yang, J., Gu, X., Shi, J., & Li, H. (2026). Interpretation of soil deformation characteristics based on in-situ test: A comprehensive review. Ocean Engineering, 352, 124508.
Huang, Jingya, et al. "Probabilistic Co-Control in Brain-Computer Interfaces: Uncertainty as a Control Signal in Brain-to-Text Decoding." bioRxiv (2026): 2026-04.
Yang, Y., Robertson, W. S., Jafari, A., & Arjomandi, M. (2025). Advanced numerical approaches for magnetic force calculations: a comprehensive review. Progress In Electromagnetics Research B, 115, 78-94.
Bardeen, J., Cooper, L. N., & Schrieffer, J. R. (1957). Theory of superconductivity. Physical Review, 108(5), 1175–1204.
Yang, Y., Robertson, W. S., Jafari, A., & Arjomandi, M. (2026). A four-coil magnetic levitation force sensor system for contactless horizontal force perturbations measurement. Sensors and Actuators A: Physical, 412, 118606.
Qu, W., Wang, J., Gong, Y., Huang, X., & Xiao, L. (2025, June). An end-to-end robust point cloud semantic segmentation network with single-step conditional diffusion models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 27325-27335). IEEE.
Li, Yan, et al., 2026. Mobile sensing discovery of when where and why vulnerable road
users break traffic rules. NPJ Sustain. Mob. Trans. 3 (1), 1.
Wang, X., Li, X., Shao, J., & Zhang, J. (2023). AC2C: Adaptively controlled two-hop communication for multi-agent reinforcement learning. arXiv preprint arXiv:2302.12515.
Li, R., Xiao, Z., & Zeng, Y. (2023). Toward seamless sensing coverage for cellular multi-static integrated sensing and communication. IEEE Transactions on Wireless Communications, 23(6), 5363-5376.
Wang, X., Pu, H., Kim, H. J., & Li, H. (2024). Deepsafempc: Deep learning-based model predictive control for safe multi-agent reinforcement learning. arXiv preprint arXiv:2403.06397.
Ouyang, Z., & Mayne, P. W. (2017, September). Spherical cavity expansion nexus between CPTu and DMT in soft-firm clays. In 19th International Conference on Soil Mechanics and Geotechnical Engineering (pp. 631-634). Korea Geotechnical Society, Seoul, Korea.
Zhang, Y., Pleus, A., & Althoff, M. (2026, July). Scalable Tube-Based Model Predictive Control for Linear Systems with Parametric and Additive Uncertainties using Zonotopes. In 2026 European Control Conference (ECC) (pp. 509-516). IEEE.
Hutchinson, S., Hager, G. D., & Corke, P. I. (1996). A tutorial on visual servo control. IEEE Transactions on Robotics and Automation, 12(5), 651–670.
Wang, X., Ma, X., Zhu, P., Ng, W. S., Zhang, H., Xia, X., ... & Au, K. W. S. (2024). Design, optimization, and experimental validation of a handheld nonconstant-curvature hybrid-structure robotic instrument for maxillary sinus surgery. IEEE/ASME Transactions on Mechatronics, 29(4), 3074-3082.
Ye, H.; Song, Y. Prescribed-time tracking control of MIMO nonlinear systems with nonvanishing uncertainties. IEEE Trans. Autom.
Contr. 2023, 68, 3664–3671.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Julian J. Bennett, Alexandra Sanders
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This article is licensed under the Creative Commons Attribution 4.0 International License. This license permits use, sharing, and adaptation of the article for non-commercial purposes, provided the original authors and source are properly credited.
Alternatively, you may choose other Creative Commons licenses based on your preferences:
-
CC Attribution-NonCommercial-NoDerivatives 4.0: This license allows others to download the works and share them with others as long as they credit the author, but they can’t change them in any way or use them commercially.
-
CC Attribution-NonCommercial 4.0: This license allows others to remix, tweak, and build upon the work non-commercially, as long as they credit the author, but they cannot use the material for commercial purposes.
-
CC Attribution-NonCommercial-ShareAlike 4.0: This allows others to remix, tweak, and build upon the work non-commercially, as long as they credit the author and license their new creations under identical terms.
These terms ensure that your article is freely accessible and usable, while respecting the rights of the original authors.
