Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-33-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jan 2026
Research article |
| 30 Jan 2026
| 30 Jan 2026
A data-driven dynamic modeling method for servo actuators
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Cited articles
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Baş, H. and Karabacak, Y. E.: Triboinformatic modeling of the friction force and friction coefficient in a cam-follower contact using machine learning algorithms, Tribol. Int., 181, 108336, https://doi.org/10.1016/j.triboint.2023.108336, 2023b.
Brunton, S. L. and Kutz, J. N.: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781108380690, 2019.
Chen, J., Zhu, R., Chen, W., Li, M., Yin, X., and Dai, G.: Nonlinear dynamic modeling and analysis of helical gear system with time-varying backlash caused by mixed modification, Nonlinear Dynam., 111, 1193–1212, https://doi.org/10.1007/s11071-022-07872-y, 2023.
Cheng, S., Hu, B.-B., Wei, H.-L., Li, L., and Lv, C.: Deep Learning-Based Hybrid Dynamic Modeling and Improved Handling Stability Assessment for Autonomous Vehicles at Driving Limits, IEEE T. Veh. Technol., 74, 5582–5593, https://doi.org/10.1109/TVT.2024.3515209, 2025.
