A Robust DPCC for IPMSM Based on a Full Parameter Identification Method

Wang, Yizhe; Liao, Wu; Huang, Sheng; Zhang, Ji; Yang, Meizhou; Li, Chengxu; Huang, Shoudao

doi:10.1109/tie.2022.3212371

Cited by 20 publications

(4 citation statements)

References 25 publications

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“…Regarding the first approach, its performance often depends on the convergence accuracy and speed of the online parameter identification algorithm. Additionally, in cases where initial parameter errors exist, severe current fluctuations in the DPCC current loop due to errors can adversely affect the convergence performance of the online identification algorithm, making the design and analysis of this approach more complex [12]. For the second approach, the robust control of DPCC is achieved by designing observers to observe and compensate for the aggregated disturbances caused by parameter errors [13,14].…”

Section: ⋆⋆ ⋆ ⋆⋆⋆mentioning

confidence: 99%

Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm

Fang,

Chen

2024

Energies

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This article proposes an improved model-free deadbeat predictive current control (MFCC) method for permanent magnet synchronous motors (PMSMs) based on the ultralocal model and H∞ norm. Firstly, the traditional deadbeat predictive current control (DPCC) method is introduced and a theoretical analysis is conducted on its sensitivity to parameters. Building upon this, the limitations of model dependence and the limited robustness of the deadbeat predictive current control method based on the extended state observer (ESO-DPCC) are theoretically analyzed. Furthermore, an improved MFCC method based on the ultralocal model is proposed, and the influence of the observer on MFCC is theoretically analyzed. This study combined the proposed method with the H∞ norm, and the optimal coefficients of the observer were tuned to enhance the robustness and dynamic performance of the current loop. Finally, the proposed algorithms were validated on a 400 W PMSM platform.

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Section: ⋆⋆ ⋆ ⋆⋆⋆mentioning

confidence: 99%

Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm

Fang,

Chen

2024

Energies

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“…The parameter identification method in this article is based on the voltage equations in the steady state of the PMSM. To facilitate the analysis, the motor voltage equation (1), which ignores the effect of core saturation and losses of PMSM, is considered in the dq rotating coordinate system.…”

Section: Parameter Identification Of Pmsmmentioning

confidence: 99%

“…Permanent magnet synchronous motors (PMSMs) are widely used in industry due to their high efficiency and small size. [1][2][3] . However, realizing the high-performance control for PMSMs requires accurate motor parameters.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Raccoon Optimization Algorithm for PMSM Electrical Parameter Identification

Hu,

Zhan,

et al. 2024

Preprint

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This article proposed an improved algorithm for the parameter identification of permanent magnet synchronous motors (PMSMs).An enhanced raccoon optimization algorithm (EROA) was formed by combining the raccoon optimization algorithm (ROA) with the adaptive exploration radius strategy, raccoon-washing-food-inspired strategy, and escaping-predator strategy.First, using some of the functions in IEEE CEC2015, the EROA solution has a large improvement in convergence speed and solution accuracy compared with other algorithms.Second, the EROA solution is more stable under the same conditions as demonstrated by MATLAB parameter identification simulation.Finally, EROA applies to motor parameter identification through motor control experiments.

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“…In [21], the true inductance and flux linkage of the motor are obtained by a function of switching state, compensating the parametric errors of the system. [22] combines the high-frequency current injection method to obtain the motor inductance, flux linkage, and resistance values and uses the parameters obtained by discrimination for deadbeat control to obtain a parameter-corrected reference voltage vector. The method of obtaining the real parameters of the motor through parameter identification increases the complexity of the algorithm and reduces the dynamic response of the system as a result of parameter regulation and convergence.…”

Section: Introductionmentioning

confidence: 99%

A Robust Model Predictive Current Control Strategy with Low Complexity for PMSM

Xiao,

Tang,

Zhang

et al. 2023

PIER C

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Model predictive current control (MPCC) suffers from high computational effort, and control performance is affected by parameter mismatch. In this paper, a robust MPCC strategy with low complexity for permanent magnet synchronous motor (PMSM) is proposed, which reduces the computational complexity and improves robustness. First, a low-pass filter is used to obtain the current actual voltage, and the next-cycle voltage vector is obtained by angle compensation. Alternative voltage vectors (AVVs) are selected according to the location of the next-cycle voltage vector to reduce the control system computation. This part does not use motor parameters to avoid the influence of parameter changes. Then, the relationship between the current error and the input voltage and current sampling value is analysed. A low-complexity current prediction error compensation algorithm is designed to compensate the error caused by the mismatch of motor inductance and flux linkage, which enhances the robustness of the system. Finally, the experimental results demonstrate the correctness and effectiveness of the proposed strategy.

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A Robust DPCC for IPMSM Based on a Full Parameter Identification Method

Cited by 20 publications

References 25 publications

Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm

Improved Model-Free Deadbeat Predictive Current Controller for PMSMs Based on Ultralocal Model and H∞ Norm

Enhanced Raccoon Optimization Algorithm for PMSM Electrical Parameter Identification

A Robust Model Predictive Current Control Strategy with Low Complexity for PMSM

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