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Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer

Liu, Huixian, Li, Shihua·2012·IEEE Transactions on Industrial Electronics

Abstract

The speed regulation problem for permanent magnet synchronous motor (PMSM) servo system is studied in this paper. In order to optimize the control performance of the PMSM servo system, the predictive functional control (PFC) method is introduced in the control design of speed loop. The PFC-based speed control design consists of two steps. A simplified model is employed to predict the futureq-axis current of PMSM. Then, an optimal control law is obtained by minimizing a quadratic performance index. However, it is noted that the standard PFC method does not achieve a satisfying effect in the presence of strong disturbances. To this end, an improved PFC method, called the PFC+ESO method, is developed. It introduces extended state observer (ESO) to estimate the lumped disturbances and adds a feedforward compensation item based on the estimated disturbances to the PFC speed controller. Simulation and experiment comparisons are made for these PFC methods and proportional-integral method with antiwindup control method to verify the effectiveness of the proposed methods.

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Metadata

Authors
Liu, H., & Li, S.
Venue
IEEE Transactions on Industrial Electronics
Published
2012-02
Volume
59
Issue
2
Pages
1171-1183
ISSN
  • 0278-0046 (ISSN-L)
  • 1557-9948
OpenAlex
W2135216999
Cited by
774
References
0
FWCI
22.95
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer. https://app.cochat.ai/papers/10.1109/tie.2011.2162217
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Liu, H., & Li, S. (2012). Speed Control for PMSM Servo System Using Predictive Functional Control and Extended State Observer. IEEE Transactions on Industrial Electronics. https://doi.org/10.1109/tie.2011.2162217

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.