Model Predictive Current Control With Variable Gain Adaptive Observer Based on Current Augmenter Prediction Model for IPMSM Drives
Model Predictive Current Control With Variable Gain Adaptive Observer Based on Current Augmenter Prediction Model for IPMSM Drives
复制标题
基于电流增强器预测模型的IPMSM驱动系统变增益自适应预测电流控制
DOI：
10.1109/tvt.2022.3163733
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发表时间：
2022-06
影响因子：
6.8
通讯作者：
Chen Li;Yan Yan-Yan;Zhiqiang Wang;Qiang Geng;T. Shi;C. Xia
中科院分区：
文献类型：
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作者：
Chen Li;Yan Yan-Yan;Zhiqiang Wang;Qiang Geng;T. Shi;C. Xia
提出了一种基于电流增强器预测模型（CAPM）的适用于高速工况下内置永磁同步电动机的模型预测电流控制方法。通过考虑转子在一个控制周期内的运动，特别是在高速工况下，提高了CAPM的精度。同时，消除了永磁通链的使用。为了进一步提高MPCC方法的鲁棒性，设计了基于CAPM的自适应观测器对模型误差进行估计。所提出的自适应观测器也消除了永磁链的使用，因此具有自适应观测器的MPCC方法在整个实现过程中是独立于永磁链的。此外，由于固定自适应积分增益的自适应观测器在电机的不同运行条件下无法获得满意的性能，本文考虑了永磁同步电机的电流估计误差、电流幅值和角速度，设计了一种可变自适应积分增益的计算方法。最后，在一个20千瓦的测试平台上进行了实验验证。
This paper proposes a model predictive current control (MPCC) method based on current augmenter prediction model (CAPM) appropriate for interior permanent magnet synchronous motor (IPMSM) under high-speed condition. The accuracy of CAPM is improved by considering rotor movement during one control period especially under high-speed condition. Meanwhile, the use of the permanent magnetic flux linkage is eliminated. In order to further improve the robustness of the MPCC method, an adaptive observer based on CAPM is designed to estimate the model error. The proposed adaptive observer also eliminates the use of the permanent magnetic flux linkage, so the whole implementation process of the MPCC method with adaptive observer is independent of the permanent magnetic flux linkage. Moreover, since the adaptive observer with fixed adaptive integral gain cannot achieve satisfactory performance under different operating conditions of the motor, this paper designs a calculation method for the variable adaptive integral gains by considering the current estimation error, the amplitude of current and the angular velocity of the IPMSM. Finally, the proposed method is validated by means of the experimental results on a 20-kW test platform.