Improved Deadbeat Predictive Current Control With Model-Aid Extended State Observer for CSI Fed PMSM

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Abstract
The conventional deadbeat predictive current control (DPCC) for current-source inverter-fed permanent magnet synchronous motor (CSI-PMSM) deteriorates control performance when subjected to disturbances such as model parameter mismatch. Therefore, the model-aided extended state observer (MAESO) CSI-DPCC is proposed. First, a high precision discrete time CSI-PMSM prediction model is established, integrated with an ESO to combine model predictive capabilities with lumped disturbance estimation, thereby enhancing state variable prediction accuracy. The MAESO effectively enhances parameter robustness. Meanwhile, predicting known states through the prior model reduces the observer computational burden and increases the system control bandwidth. To eliminate the tracking error caused by disturbances, the system disturbance estimated by MAESO is fed back to the deadbeat model controller, which can estimate and compensate for internal and external disturbances in real time without requiring an additional integrator to eliminate steady-state errors. In addition, the parameter design method of MAESO-CSI-DPCC is proposed based on the Jury stability criterion for discrete systems. Finally, comparative simulations and experiments between the CSI-DPCC and the proposed MAESO-CSI-DPCC verify the effectiveness and superiority of the proposed strategy.
Funding Information
  • National Natural Science Foundation of China (52377042)

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