Event‐Triggered Asymptotic Nussbaum Design for Permanent Magnet Synchronous Motor Systems With Multiple Unknown Control Coefficients

ABSTRACT Due to the coupling characteristic of permanent magnet synchronous motor (PMSM) systems, this paper considers the influence of the direct current component on the angular velocity as multiple unknown control coefficients for asymptotic control design. On this basis, to ensure the asymptotic stability of the PMSM, improvements to the traditional Nussbaum method are developed as follows. To guarantee the asymptotic tracking performance of the Nussbaum approach, novel Nussbaum gains are designed with decaying terms, which enable the residual terms to converge to zero. Considering the presence of unpredictable load disturbances, a compensation term is introduced into the Nussbaum‐type gain to mitigate the impact on the dynamics of the angular velocity. Under the Nussbaum‐based approach, an event‐triggered control mechanism is proposed to reduce the update frequency of the control signals. A dynamic triggering threshold is designed to guarantee that the influence of intermittent feedback on system dynamics remains exponentially bounded. Different from the single‐input and single‐output dynamic systems, the PMSM system involves multiple inputs. Therefore, separate triggering conditions are designed for the direct and quadrature voltage components, achieving asynchronous intermittent feedback updates. The Nussbaum‐based event‐triggered design achieves precise tracking in the presence of coupling, parameter uncertainties, multiple unknown control coefficients, and load variation. Finally, simulation studies are presented to validate the effectiveness of the proposed adaptive event‐based Nussbaum control method.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-11-04
DOI
https://doi.org/10.1002/rnc.70265
Primary Topic
Iterative Learning Control Systems
Type
article
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article

Event‐Triggered Asymptotic Nussbaum Design for Permanent Magnet Synchronous Motor Systems With Multiple Unknown Control Coefficients

Yongliang Yang, Guofeng Yuan, Chunyu Zheng, Z. S. Shan et al.
International Journal of Robust and Nonlinear Control
Iterative Learning Control Systems
article

Event‐Triggered Asymptotic Nussbaum Design for Permanent Magnet Synchronous Motor Systems With Multiple Unknown Control Coefficients

Yongliang Yang, Guofeng Yuan, Chunyu Zheng, Z. S. Shan, Hu Zhang
article en

Abstract

ABSTRACT Due to the coupling characteristic of permanent magnet synchronous motor (PMSM) systems, this paper considers the influence of the direct current component on the angular velocity as multiple unknown control coefficients for asymptotic control design. On this basis, to ensure the asymptotic stability of the PMSM, improvements to the traditional Nussbaum method are developed as follows. To guarantee the asymptotic tracking performance of the Nussbaum approach, novel Nussbaum gains are designed with decaying terms, which enable the residual terms to converge to zero. Considering the presence of unpredictable load disturbances, a compensation term is introduced into the Nussbaum‐type gain to mitigate the impact on the dynamics of the angular velocity. Under the Nussbaum‐based approach, an event‐triggered control mechanism is proposed to reduce the update frequency of the control signals. A dynamic triggering threshold is designed to guarantee that the influence of intermittent feedback on system dynamics remains exponentially bounded. Different from the single‐input and single‐output dynamic systems, the PMSM system involves multiple inputs. Therefore, separate triggering conditions are designed for the direct and quadrature voltage components, achieving asynchronous intermittent feedback updates. The Nussbaum‐based event‐triggered design achieves precise tracking in the presence of coupling, parameter uncertainties, multiple unknown control coefficients, and load variation. Finally, simulation studies are presented to validate the effectiveness of the proposed adaptive event‐based Nussbaum control method.

International Journal of Robust and Nonlinear ControlVol. 36(4)
North China University of Technology (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 59%
Iterative Learning Control Systems
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