Recent Advances in Nonlinear Dynamics Applied in Electromechanical Systems
Source abstract
Abstract
Electromechanical equipment is indispensable in the modernization. The motions of electromechanical systems are becoming increasingly complex, which inevitably involves nonlinearity. The recent advances in design, analysis and control are to improve dynamic performance, steady-state stability, and anti-interference ability to meet modern industry upgrading requirements. In recent years, with the increasing complexity of application scenarios, one has higher anticipation for the nonlinear dynamic performance, intelligence, and automation of electromechanical systems, which also brings new challenges to the research, design, and control of electromechanical systems. Therefore, how to combine nonlinear dynamics, artificial intelligence, and automation with the research, design, and control of the electromechanical system has become a hot issue. The advanced analysis, design and control methods contribute to develop and improve nonlinear electromechanical systems with high stability and dynamic performance. We gather the recent advances on nonlinear dynamics and control of electromechanical systems to provide new perspectives to the research and design of the future products.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dynamics and Control of Mechanical Systems · Control Systems in Engineering · Adaptive Control of Nonlinear Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Aerospace Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Department of Mechatronics Engineering, Harbin Institute of Technology, Harbin 150001, China
Funding
the key laboratory for safety of hydrogen storage and transportation equipment of state aministration for market regulation of china · the key R&D and transformation plan project of Inner Mongolia autonomous region · the special project for eancing research capacity in clinical medicine, the affiliated Inner Mongolia Medical university
References (37)
Show source-provided references
1. Dynamics and mechanical and electromechanical systems10.1063/1.3022132 ↗
2. Melcher, J.R., and Woodson, H.H. (1968). Electromechanical Dynamics. Part I: Discrete Systems, John Wiley & Sons, Inc.
3. Dynamic micromechanics on silicon: Techniques and devices10.1109/T-ED.1978.19259 ↗
4. Dynamic analysis of micro-electro-mechanical systems10.1002/(SICI)1097-0207(19961230)39:24<4119::AID-NME42>3.0.CO;2-4 ↗
5. Equivalent circuit representation of electromechanical transducers: I. Lumped-parameter systems10.1088/0960-1317/6/1/036 ↗
6. Electromechanical model of a resonatingnano-cantilever-based sensor for high-resolution and high-sensitivity mass detection10.1088/0957-4484/12/2/305 ↗
7. Preumont, A. (2006). Mechatronics: Dynamics of Electromechanical and Piezoelectric Systems, Springer.
8. Erturk, A. (2009). Electromechanical Modeling of Piezoelectric Energy Harvesters, Virginia Tech.
9. Electromechanical modeling and nonlinear analysis of axially loaded energy harvesters10.1115/1.4002786 ↗
10. Advanced model for dynamic analysis of electromechanical devices10.1109/TMAG.2004.840367 ↗
11. Dynamic range of nanotube-and nanowire-based electromechanical systems10.1063/1.1929098 ↗
12. Dynamic analysis for the cutting electromechanical transmission system in the long-wall shearer10.3901/JME.2016.07.014 ↗
13. Crowder, R. (2019). Electric Drives and Electromechanical Systems: Applications and Control, Butterworth-Heinemann.10.1016/B978-0-08-102884-1.00001-7 ↗
14. Paired asymmetric periodic oscillations in a pair of first-order asymmetric nonlinear circuit systems10.1016/j.ymssp.2022.108810 ↗
15. Wang, M., Wang, T., Lu, D., and Cui, S. (2024). Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference. Appl. Sci., 14.10.3390/app14219643 ↗
16. Lu, D., Wang, M., Xu, Y., Wang, X., and Wang, S. (2024). Analytical Determination of Stick–Slip Whirling Vibrations and Bifurcations in Rotating Machinery. Appl. Sci., 14.10.3390/app14167338 ↗
17. Xu, B., Ning, P., Wang, G., and Zang, C. (2024). Steady-State Response Analysis of an Uncertain Rotor Based on Chebyshev Orthogonal Polynomials. Appl. Sci., 14.10.3390/app142210698 ↗
18. Liu, Y., Yang, Z., Mao, T., and Li, W. (2024). Complex Periodic Motions and Bifurcations of a Forced Duffing Oscillator with Its Field-Programmable Gate Arrays Implementation. Appl. Sci., 14.10.3390/app142311243 ↗
19. Zhang, Y., Zheng, T., Zhou, Z., and Fu, W. (2025). Design and Control of an Active–Passive Integrated Six-Dimensional Orthogonal Vibration Isolation Platform. Appl. Sci., 15.10.3390/app15073437 ↗
20. A comprehensive overview of high-speed solid-rotor induction machines: Applications, classification, and multi-physics modeling10.1016/j.ijepes.2025.110520 ↗
21. Advanced multi-physics modeling of floating offshore wind turbines for aerodynamic design and load management10.1016/j.enconman.2025.120437 ↗
22. Vehicle lateral dynamics-inspired hybrid model using neural network for parameter identification and error characterization10.1109/TVT.2024.3416317 ↗
23. Review of thermal coupled battery models and parameter identification for lithium-ion battery heat generation in EV battery thermal management system10.1016/j.ijheatmasstransfer.2023.124748 ↗
24. Mörée, G., and Leijon, M. (2023). Review of hysteresis models for magnetic materials. Energies, 16.10.3390/en16093908 ↗
25. Investigation of magnetic hysteresis in HTS coil by means of numerical modelling using TA formulation10.1088/1361-6668/ae1260 ↗
26. Composite Poincaré mapping of double grazing in non-smooth dynamical systems: Bifurcations and insights10.1007/s00707-023-03602-6 ↗
27. Numerical and experimental study of vibrations in a non-smooth electromechanical system10.1016/j.jmmm.2023.171638 ↗
28. B-spline wavelet neural-network-based adaptive control for linear-motor-driven systems via a novel gradient descent algorithm10.1109/TIE.2023.3260318 ↗
29. Intelligent robot path planning and navigation based on reinforcement learning and adaptive control
30. Zhang, B., and Liu, L. (2023). Chaos-based image encryption: Review, application, and challenges. Mathematics, 11.10.3390/math11112585 ↗
31. Ultra-chaos of a mobile robot: A higher disorder than normal-chaos10.1016/j.chaos.2022.113037 ↗
32. Reducing computational complexity of neural networks in optical channel equalization: From concepts to implementation10.1109/JLT.2023.3234327 ↗
33. Computational complexity optimization of neural network-based equalizers in digital signal processing: A comprehensive approach10.1109/JLT.2024.3386886 ↗
34. Recent progress in sensor arrays: From construction principles of sensing elements to applications10.1021/acssensors.2c02596 ↗
35. In-sensor computing: Materials, devices, and integration technologies10.1002/adma.202203830 ↗
36. Reliability analysis of the multi-state system with nonlinear degradation model under Markov environment10.1016/j.ress.2023.109411 ↗
37. Dynamic design of a magnetic-enhanced nonlinear energy sink10.1016/j.ymssp.2022.109813 ↗