IEEE Journal of Emerging and Selected Topics in Power Electronics · 2019 · 45 citations · 25 references
EngineeringElectrical EngineeringElectric MachinesMotor ControlPower ElectronicsDead-time CompensationElectric DrivesElectric Motor Drive SystemsElectric Motor DrivesElectric MotorsSynchronous MachinesBack Electromotive ForceCurrent ControlElectric Motor ControlMoving Horizon EstimationPermanent Magnet MotorsPermanent Magnet Synchronous MotorsPredictive ControlSynchronous Machine Modeling
The deadbeat predictive current control (DPCC) of permanent-magnet synchronous motors (PMSMs) is well known for its simple structure and fast dynamic. However, it is sensitive to parameter mismatches and system delays, which becomes more serious at the low switching frequency. To handle such problems, this article proposes an accurate deadbeat control method based on an exact model of surface-mounted PMSM in a stationary two-phase frame with a moving horizon estimator (MHE). The motor model is obtained from differential equations, and transformations between stationary frame and rotating frame are reduced to eliminate the influence of the lag of electrical angle. MHE is utilized to estimate the back-electromotive force (EMF) and disturbance caused by parameter variations. The rotor flux is eliminated in the algorithm and the resistance and inductance mismatches are compensated by the estimator. Several simulations and experiments are performed in three methods-the conventional DPCC in rotating <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$dq $ </tex-math></inline-formula> -axis, DPCC in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula> -axis with the accurate model, and the proposed method. These comparisons validate that the proposed method has good current tracking performance at low switching frequency and a strong robustness to parameter variations.
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