Lumped Parameter Adaptation-Based Automatic MTPA Control for IPMSM Drives by Using Stator Current Impulse Response

At present, to ensure the optimal operating point tracking accuracy and the dynamic performance of the automatic maximum torque per ampere (MTPA) control for interior permanent magnet synchronous machines (IPMSM), additional sensors or observers are usually required, which undoubtedly increases the cost and complexity of the system. Moreover, for state-of-the-art automatic MTPA control methods, the filters are usually needed for signal extraction, thus, the robustness of the system will be affected by signal distortion and filter noise. To deal with these problems, this paper proposes a lumped parameter adaptation-based automatic MTPA control for IPMSM drives by using stator current impulse response. The stator current response to the lumped parameter impulse excitation is utilized to adaptively adjust the estimated lumped parameter to its actual value. Then, the MTPA operating points can be calculated according to the lumped parameter-based control equation, thus, the automatic MTPA control can be achieved. Benefiting from the use of the stator current impulse response, the resultant response component can be directly extracted by signal differentiation without the requirement of filters, additional sensors or observers. Thus, the proposed method has better stability and dynamic performance than the conventional automatic MTPA control method while reducing the cost and complexity of the system. The validity of the proposed method is confirmed by both the simulation and experiments.

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Institutions

Publication Details

Journal
IEEE Transactions on Energy Conversion
Published
2025-03-11
DOI
https://doi.org/10.1109/tec.2025.3550193
Citations
21
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
15.34

Funders

Controls
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article

Lumped Parameter Adaptation-Based Automatic MTPA Control for IPMSM Drives by Using Stator Current Impulse Response

Shichuan Ding, Jun Hang, Chulong Liu, Zhixiang Fan
21 citations
IEEE Transactions on Energy Conversion
Sensorless Control of Electric Motors
15.34
article

Lumped Parameter Adaptation-Based Automatic MTPA Control for IPMSM Drives by Using Stator Current Impulse Response

Shichuan Ding, Jun Hang, Chulong Liu, Zhixiang Fan
article en
21 citations

Abstract

At present, to ensure the optimal operating point tracking accuracy and the dynamic performance of the automatic maximum torque per ampere (MTPA) control for interior permanent magnet synchronous machines (IPMSM), additional sensors or observers are usually required, which undoubtedly increases the cost and complexity of the system. Moreover, for state-of-the-art automatic MTPA control methods, the filters are usually needed for signal extraction, thus, the robustness of the system will be affected by signal distortion and filter noise. To deal with these problems, this paper proposes a lumped parameter adaptation-based automatic MTPA control for IPMSM drives by using stator current impulse response. The stator current response to the lumped parameter impulse excitation is utilized to adaptively adjust the estimated lumped parameter to its actual value. Then, the MTPA operating points can be calculated according to the lumped parameter-based control equation, thus, the automatic MTPA control can be achieved. Benefiting from the use of the stator current impulse response, the resultant response component can be directly extracted by signal differentiation without the requirement of filters, additional sensors or observers. Thus, the proposed method has better stability and dynamic performance than the conventional automatic MTPA control method while reducing the cost and complexity of the system. The validity of the proposed method is confirmed by both the simulation and experiments.

IEEE Transactions on Energy ConversionVol. 40(3)
Anhui University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 1%
Sensorless Control of Electric Motors
15.34
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