A method for dtc predictive control based on support vector machine
A technology of support vector machine and predictive control, which is applied in the direction of electronic commutation motor control, vector control system, motor generator control, etc., can solve the problems of lack of theoretical support and large amount of calculation, so as to avoid calculation amount, improve performance, The effect of reducing the amount of related calculations
Active Publication Date: 2021-04-06
CHANGAN UNIV
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Problems solved by technology
However, there are still some problems in predictive control. Firstly, the scientificity of its cost function is still to be studied. Secondly, in order to make the torque error and flux error on the same dimension, weight coefficients are usually introduced, and the selection of weight coefficients mostly depends on experience. Lack of strong theoretical support
At the same time, the use of predictive control requires the calculation of the cost of six basic voltage vectors at the same time, which requires a huge amount of calculation, which is contrary to the original intention of introducing direct torque control
Method used
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Embodiment 1
[0058] First collect the parameters of the motor operation using the cost function for voltage vector selection, including the stator flux error ψ r , output torque error T r , the load angle δ, the torque angle α and the selected voltage vector, the sampling frequency is ten times the frequency used by the motor, and the sampled data is normalized, as shown in formula (2). The support vector machine using formula (3) as the kernel function is used for offline training, and the cross-validation method is used to obtain the model with the highest accuracy. The calculation method of accuracy is shown in formula (4), and the model with the highest accuracy is used as the basis A support vector machine is used for the prediction model of DTC.
[0059] Secondly, when the motor is running, the stator flux error ψ at a certain moment r , output torque error T r , the load angle δ and the torque angle α are used as input, and are input into the prediction model through normalizatio...
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The invention relates to a method for DTC predictive control based on a support vector machine, which first selects existing motor operating parameters as input and output samples; performs normalization processing on the selected input and output samples; normalization processing The final sample is trained to obtain the prediction model based on the support vector machine of the output voltage vector for DTC; the operating parameters of the current motor are normalized and then input into the prediction model, and the applied basic voltage vector is output through the prediction model; According to the basic voltage vector output by the predictive model, the switching state of the inverter is controlled, and the DTC predictive control based on the support vector machine is completed. The invention can reduce related calculation amount, reduce torque ripple, and keep switching frequency constant.
Description
technical field [0001] The invention belongs to the field of motor control, and in particular relates to a method for DTC predictive control based on a support vector machine. Background technique [0002] Direct torque control (DTC) controls the electromagnetic torque by controlling the magnitude of the stator flux linkage and the load angle. Permanent magnet synchronous motor direct torque control usually uses two hysteresis comparators to control the stator flux linkage and torque deviation respectively. Since it avoids a large number of calculations in the rotating coordinate system, its dynamic performance will be improved and the response will be faster . [0003] In the permanent magnet synchronous motor direct torque control system realized by the traditional switch table, the voltage vector is continuously applied within a sampling period, and the actual torque increase or decrease exceeds the expected requirement, resulting in overshoot pulsation. [0004] In ord...
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IPC IPC(8): H02P21/00H02P21/30H02P27/12H02P6/10
CPCH02P6/10H02P21/0017H02P27/12H02P21/30
Inventor 李耀华周逸凡秦玉贵赵承辉秦辉苏锦仕
Owner CHANGAN UNIV