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Model predictive control method of surface permanent magnet synchronous motor based on bp neural network

A technology of BP neural network and permanent magnet synchronous motor, 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 achieve accelerated training and high accuracy And the effect of satisfactory effect, powerful nonlinear fitting and adaptive self-learning ability

Active Publication Date: 2021-03-23
黑龙江省汉泽石油设备有限公司
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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 permanent magnet synchronous motor based on bp neural network
  • Model predictive control method of surface permanent magnet synchronous motor based on bp neural network
  • Model predictive control method of surface permanent magnet synchronous motor based on bp neural network

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Embodiment Construction

[0055] The present invention will be further described below in conjunction with the accompanying drawings.

[0056] see figure 1 , the present invention comprises the following steps:

[0057] Step 1, determine the input and output in the model predictive control algorithm of the surface type permanent magnet synchronous motor, and determine the value range and appropriate change step size of the change;

[0058] Step 2: Traversing the value range of each parameter according to the change step size of each input, and sending it into the model predictive control algorithm of the surface type permanent magnet synchronous motor to generate the optimal model predictive control algorithm selection in the future control cycle. The voltage vector sequence, and the value of each input quantity and the corresponding selected optimal voltage vector are combined into training samples of the BP neural network;

[0059] Step 3, constructing a BP neural network topology model;

[0060] ...

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Abstract

The invention discloses a surface type permanent magnet synchronous motor model prediction control method based on BP neural network. The present invention uses BP neural network to replace the surface type permanent magnet synchronous motor model prediction algorithm. The BP neural network has powerful nonlinear fitting and self-adaptive The ability of self-learning can greatly reduce the calculation time and calculation burden of the algorithm, improve the timeliness of the system, and the accuracy and effect of BP neural network replacement are satisfactory. Compared with the traditional model prediction algorithm, it has certain innovative advantages. The application prospect of intelligent algorithm in motor control is verified.

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): H02P21/00H02P21/30G06N3/08G06N3/04
CPCH02P21/0014H02P21/30G06N3/08G06N3/044
Inventor 李耀华赵承辉秦玉贵周逸凡秦辉苏锦仕
Owner 黑龙江省汉泽石油设备有限公司
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