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Surface permanent magnet synchronous motor model predictive control method 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 problems such as difficult implementation and large amount of calculation of prediction algorithms, and achieve accuracy and satisfactory results , speed up training, and improve timeliness

Active Publication Date: 2019-09-20
CHANGAN UNIV
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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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  • Surface permanent magnet synchronous motor model predictive control method based on BP neural network
  • Surface permanent magnet synchronous motor model predictive control method based on BP neural network
  • Surface permanent magnet synchronous motor model predictive control method 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 permanent magnet synchronous motor model predictive control method based on a BP neural network. A BP neural network is adopted to replace a surface permanent magnet synchronous motor model prediction algorithm. With strong nonlinear fitting and adaptive self-learning ability, the BP neural network can greatly reduce the operation time and operation burden of the algorithm and improve the timeliness of the system. Moreover, the accuracy and effect of the BP neural network are satisfactory. The method of the invention has innovation advantage compared with the traditional model prediction algorithm, and 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 Applications(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 CHANGAN UNIV
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