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Model Predictive Control Method of Surface PMSM Based on BP Neural Network

A technology of permanent magnet synchronous motor and BP neural network, which is applied in biological neural network models, neural learning methods, motor generator control, etc., can solve the problems of large amount of calculation and difficult implementation of prediction algorithms, and improve timeliness and accuracy Satisfied with the efficiency and effect, the effect of reducing the operation time

Inactive Publication Date: 2021-03-30
CHANGAN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the prediction algorithm proposed in the literature has a large amount of computation, which is difficult to implement in practical applications.

Method used

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  • Model Predictive Control Method of Surface PMSM Based on BP Neural Network
  • Model Predictive Control Method of Surface PMSM Based on BP Neural Network
  • Model Predictive Control Method of Surface PMSM Based on BP Neural Network

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Experimental program
Comparison scheme
Effect test

Embodiment

[0114] Based on MATLAB / Simulink, a surface type permanent magnet synchronous motor model prediction direct torque control simulation model is established. The simulation model is a discrete model, and the sampling period is 5×10 -5 s. The DC bus voltage is 312V. The parameters of the speed PI regulator are: Kp=5, KI=10, and the upper and lower limits of the output of the PI regulator are [-35, 35]. The reference stator flux amplitude is 0.3Wb. The parameters of the surface permanent magnet synchronous motor used for simulation are shown in Table 1.

[0115] Table 1 Parameters of surface permanent magnet synchronous motor for simulation

[0116]

[0117] Considering the learning of the two changes of reference speed and load torque comprehensively, use the ramp function to create training data:

[0118] In the three cases of the reference speed of 10rpm, 30rpm and 60rpm, the load torque is set using the ramp function, the initial value is 10N·m, the simulation time is 2...

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Abstract

The invention discloses a surface type permanent magnet synchronous motor model predictive control method based on BP neural network. The invention firstly generates an optimal voltage vector sequence through the input and output values ​​in the surface type permanent magnet synchronous motor model predictive control algorithm, and then through The optimal voltage vector sequence trains the BP neural network topology model, and uses the trained BP neural network to replace the surface permanent magnet synchronous motor model prediction algorithm. The BP neural network has powerful nonlinear fitting and pattern recognition and classification capabilities, which can greatly reduce The calculation time and calculation burden of the algorithm can improve the timeliness of the system. At the same time, it has the advantages of simple structure, high precision and fast response speed. Moreover, the characteristics of distributed parallel calculation of neural network make a large number of calculations possible, which can reduce the calculation burden of the system. Improving the timeliness of system response has certain innovative advantages compared with traditional model prediction algorithms, which verifies the application prospects of intelligent algorithms in motor control.

Description

technical field [0001] The invention belongs to the field of direct torque control of permanent magnet synchronous motors, in particular to a model predictive control method for surface type permanent magnet synchronous motors based on BP neural network. Background technique [0002] The direct torque control technology is based on the stator flux coordinate system and directly takes the torque as the control object, avoiding a large number of calculations during the transformation of the rotating coordinates and the dependence on the motor parameters. It has good dynamic performance and short torque response time. However, the traditional DTC is an off-line control method. The control algorithm and the pre-programmed voltage vector LUT are implanted into the microprocessor and executed in each control cycle. According to the current torque error and stator flux error of the motor control system, DTC selects the optimal voltage vector from the voltage vector LUT to eliminate...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H02P21/00H02P21/14H02P21/30G06N3/08G06N3/04
CPCH02P21/0014H02P21/14H02P21/30G06N3/08G06N3/044
Inventor 李耀华赵承辉周逸凡秦玉贵秦辉苏锦仕
Owner CHANGAN UNIV