Robust Moment of Inertia Identification in SPMSM Systems Using Current or Low-Amplitude Speed Sinusoidal Excitation

This article proposes a novel robust method for identifying the moment of inertia of rotor shaft in surface-mounted permanent magnet synchronous motor servo systems. Superior to conventional approaches, this method enables stable identification under current control loop or poorly tuned speed loop, and can identify the moment of inertia using low-amplitude speed injection as small as 0.5 rad/s (4.8 r/min), while maintaining minimal errors. The method is based on sinusoidal signal injection and identifies the moment of inertia by analyzing the phase difference between the estimated load torque and motor speed. This approach provides a reliable one-time parameter calibration, largely insensitive to the influence of unknown disturbances, including Stribeck friction, nonlinear load, and speed quantization errors. This method can be applied to rapid autotuning of speed loop and online moment of inertia identification with low-amplitude injection, providing a foundation for high-performance control algorithms. Experimental results validate these characteristics.

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Publication Details

Journal
IEEE Transactions on Industrial Electronics
Published
2025-10-02
DOI
https://doi.org/10.1109/tie.2025.3607960
Primary Topic
Neural Networks and Applications
Type
article
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article

Robust Moment of Inertia Identification in SPMSM Systems Using Current or Low-Amplitude Speed Sinusoidal Excitation

Zhichao Chen, Zhe Liang, Yang Liang, Deliang Liang et al.
IEEE Transactions on Industrial Electronics
Neural Networks and Applications
article

Robust Moment of Inertia Identification in SPMSM Systems Using Current or Low-Amplitude Speed Sinusoidal Excitation

Zhichao Chen, Zhe Liang, Yang Liang, Deliang Liang, Yuheng Wang
article en

Abstract

This article proposes a novel robust method for identifying the moment of inertia of rotor shaft in surface-mounted permanent magnet synchronous motor servo systems. Superior to conventional approaches, this method enables stable identification under current control loop or poorly tuned speed loop, and can identify the moment of inertia using low-amplitude speed injection as small as 0.5 rad/s (4.8 r/min), while maintaining minimal errors. The method is based on sinusoidal signal injection and identifies the moment of inertia by analyzing the phase difference between the estimated load torque and motor speed. This approach provides a reliable one-time parameter calibration, largely insensitive to the influence of unknown disturbances, including Stribeck friction, nonlinear load, and speed quantization errors. This method can be applied to rapid autotuning of speed loop and online moment of inertia identification with low-amplitude injection, providing a foundation for high-performance control algorithms. Experimental results validate these characteristics.

IEEE Transactions on Industrial ElectronicsVol. 73(2)
State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University (CN)
Openalex Percentile: Top 86%
Neural Networks and Applications
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