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Stable Simultaneous Inertia and Disturbance Torque Identification for SPMSM Drive Systems Subject to Mismatched Rotor Flux Linkage
Abstract
In surface-mounted permanent magnet synchronous motor (SPMSM) drives, the distinguished orthogonal-principle-based method is presently popular for handling simultaneous inertia and disturbance torque estimation due to its simplicity and ease of implementation. Nevertheless, it is facing challenges posed by the rotor flux-linkage mismatch, zero-derivative constraint, and operating-condition limitation. To cope with these issues, this article presents an alternative scheme, which explores a stable simultaneous inertia and disturbance torque estimation with the real-time flux-linkage correction. First, an Adaline-based estimator is developed to acquire the disturbance torque. With an input-adaptive learning rate designed by the Lyapunov theory, this estimator can prevent potential convergence failure. Then, an extended sliding-mode observer for inertia estimation is proposed regarding the inertia as a new system state. By using the identified disturbance torque to devise an adaptive feedback gain, this observer guarantees the asymptotic error convergence. Finally, to counteract the flux-linkage mismatch, a flux-linkage observer with a time-varying learning rate is constructed utilizing the Adaline technique. Moreover, it attains robust convergence and only has one to-be-adjusted parameter. The effectiveness of our scheme is validated by abundant simulations and real-time experiments.
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“Space Vector Modulation (SVM) is a sophisticated pulse-width modulation technique that optimizes the use of the DC bus voltage. By generating optimal switching patterns, SVM reduces harmonic distortion and enhances the overall efficiency of the converter, particularly in high-frequency applications [109].”
From Bidirectional DC-DC Converter Topologies for Hybrid Energy Storage Systems in Electric Vehicles: A Comprehensive Review (2025) · cites this paper as [109]
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Tong, Y., Salhi, I., Wang, Q., Lu, G., & Wu, S. (2025) · Energies · doi:10.3390/en18092312 · Cited by 60
“By generating optimal switching patterns, SVM reduces harmonic distortion and enhances the overall efficiency of the converter, particularly in high-frequency applications [109].”
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Metadata
- Authors
- Yang, C., Song, B., Xie, Y., Lu, S., & Tang, X.
- Affiliations
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Guangdong Intelligent Robotics Institute, Dongguan, China
- Wuhan University of Science and Technology (CN)
- Huazhong University of Science and Technology (CN)
- Venue
- IEEE Journal of Emerging and Selected Topics in Power Electronics
- Published
- 2022-04
- Volume
- 10
- Issue
- 2
- Pages
- 2445-2462
- ISSN
- 2168-6777 (ISSN-L)
- 2168-6785
- OpenAlex
- W3197146428
- Cited by
- 29
- References
- 44
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 6 · 2023: 8 · 2022: 2
- FWCI
- 1.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Journal of Emerging and Selected Topics in Power Electronics (publishedVersion)
- Funders
- National Key Research and Development Program of China (SQ2019YFB-1707300)
- Basic and Applied Basic Research Foundation of Guangdong Province (2020A1515110464, 2021A1515012409)
- Innovative and Entrepreneurial Teams Program of Guangdong (2017ZT07G493)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Physical Sciences › Engineering › Mechanical Engineering › Control Systems in Engineering
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Keywords
- surface-mounted permanent magnet synchronous motor, disturbance torque estimation, inertia estimation, time-varying learning rate, motor drives
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