Permanent magnet synchronous motor model prediction current control method

Abstract

The invention discloses a permanent magnet synchronous motor model prediction current control method. The method comprises the following steps: the step S1, sampling stator current; the step S2, predicting a dq-axis current at a k+1 moment; the step S3, calculating effective voltage vector action time top<t>; the step S4, optimizing the effective voltage vector action time; and the step S5, selecting an optimal voltage vector Uopt. According to the control method, the voltage vector and the action time of the voltage vector are optimized together, so that the additional calculation amount is not increased, the quadrature-direct axis current ripples are remarkably reduced, and the finally selected voltage vector is globally optimal.

Description

technical field [0001] The invention relates to the field of motor control methods, in particular to a model predictive current control method for a permanent magnet synchronous motor. Background technique [0002] High power density, high efficiency and high reliability are the current development trends of motor drive systems. Due to the advantages of high power density, high efficiency, high reliability and wide speed regulation range, permanent magnet synchronous motor (PMSM) has been widely used in many fields such as electric vehicles, ship electric propulsion systems, high-speed rail traction systems, and wind power generation. [0003] At present, there are mainly two traditional control methods for permanent magnet synchronous motors: vector control and direct torque control. Traditional vector control has good steady-state performance and small torque ripple; direct torque control adopts hysteresis comparator and switch vector table, which has excellent dynamic pe...

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Problems solved by technology

[0006] 1. Vector control uses space vector modulation to generate the required voltage vector. When the system works in low carrier ratio modulation mode, the permanent magnet synchronous motor will generate a large number of current harmonics, which is not conducive to the stable operation of the system.

In addition, because the current inner loop is generally based on the proportional-integral (PI) controller design, there are problems such as integral saturation, the mutual influence of the dq-axis (dq axis) current control, and the constraints of the system are not easy to handle, resulting in the dynamic response of the current loop. limit, it is impossible to further increase the

[0007] 2. In direct torque control, the direct torque switch table is set in advance for a specific working condition, but the working condition of electric vehicles is complex, frequent and changeable, and it is difficult to maintain the best control performance of this method under its full working condition. excellent

In addition, its torque ripple is large, which cannot well meet the requirements of stable torque control for electric vehicle drives.

[0008] 3. The traditional model predictive current control method is based on a voltage vector to predict and optimize, that is, to let an effective voltage vector act on the entire sampling period, it is difficult to obtain better steady-state performance

[0009] 4. The duty cycle model predictive current control method introduces a zero voltage vector on the basis of the traditional model predictive current control method, that is, an effective voltage vector and a zero voltage vector act together in one sampling period, which improves the traditional model predictive current control method. Steady-state performance, but since the voltage vector and duty cycle are optimized separately, the selected voltage vector cannot be guaranteed to be globally optimal

[0010] To sum up, it can be seen that for model predictive control, there is still a lack of a motor optimal control method to ensure that the final selected voltage vector is globally optimal, and can reduce the AC-D axis current ripple without increasing the computational burden

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