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Current Prediction Error Based Parameter Identification Method for SPMSM With Deadbeat Predictive Current Control
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AI
DOI：10.1109/TEC.2021.3051212.png)
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摘要
En 中文
Deadbeat predictive current control (DPCC) can predict motor behavior based on SPMSM model. However, during the operation of motor system, motor parameters (such as stator inductance and flux linkage) vary frequently according to different working conditions, which may lead to controller parameter mismatch, causing current harmonic content to increase and efficiency to decrease. In order to solve these problems caused by parameter variation, first, this paper proposes a current prediction error model by considering uncertainties of model parameters. Second, stator inductance and flux linkage are decoupled based on current prediction error model, which can reduce the interaction between parameters. Finally, the Kalman Filter (KF) algorithm is presented to filter the decoupled parameters. It is shown that the stator inductance and flux linkage can be identified accurately and the complexity of computation can be simplified. The traditional DPCC method, Extended Kalman Filter (EKF) based DPCC method and the proposed DPCC method are comparatively analyzed in this paper. Simulation and experiment indicate that the proposed parameter decoupling identification method can effectively reduce current harmonic content, current fluctuation and current tracking errors caused by parameter mismatch.
Keyword:
Stators
Inductance
Couplings
Predictive models
Resistance
Mathematical model
Torque
Surface-mounted permanent magnet synchronous machine (SPMSM)
current prediction error model
Kalman Filter (KF)
parameter decoupling
parameter identification