Fuzzy fusion identification method of rotating speed of sensorless motor

Abstract

The invention discloses a fuzzy fusion identification method of rotating speed of a sensorless motor, comprising the following steps: identifying the rotating speed of the motor through neural network MRAS speed identification method and direct speed identification method of rotating difference frequency; fusing the neural network MRAS speed identification method and the direct speed identification method of rotating difference frequency through fuzzy fusion method so as to obtain the confirmation value of rotating speed of the motor. The invention has the following advantages that: the two technical indexes of dynamic and static performance of the motor are optimized; the method has quick speed identification, strong dynamic tracking performance, high speed identification precision and strong robustness during the starting and state switching processes of the motor; and the method realizes intelligent crossing comprehensive on-line speed identification of the vector control system of the sensorless motor to obtain optimal speed identification effect so as to effectively control the motor.

Description

technical field [0001] The invention relates to a motor control technology, in particular to a fuzzy fusion identification method for a motor speed without a speed sensor. Background technique [0002] In the speed control system of AC asynchronous motor (hereinafter referred to as "motor" or "motor"), the lowest-end method is to use speed sensors to detect motor speed feedback signals; these speed sensors are installed on the motor shaft, not only need to install , maintenance, and increase the cost of the control system, it is not suitable for working in harsh environments and reduces the reliability of the system. If the speed sensor is not used, only the motor speed is obtained according to the voltage and current signals output by the frequency converter for closed-loop control, the speed sensor can be omitted, and the requirements of simplicity, cheapness and reliability of the motor speed control can be met. mainstream research directions. [0003] Scholars at h...

AI Technical Summary

Problems solved by technology

[0004] The most direct way to identify the motor speed with an intelligent method is to use the easy-to-detect motor stator voltage and current to design a BP neural network to identify the speed, but this method has the problem that it is difficult to determine the hidden layer of the network and the number of nodes

At present, there is no good way to determine the specific network structure, and it is still based on experience

At the same time, the convergence speed of the learning algorithm is slow, and the convergence speed is related to the selection of the initial weight

[0005] Aiming at the problem of poor real-time speed identification using BP neural network, some scholars have proposed using diagonal recursive neural network to identify speed; such as: Yang Junyou, Chen Daming, "Sensorless Control of Permanent Magnet Synchronous Motor with Diagonal Recurrent Neural Network", [J]. Journal of Shenyang University of Technology, 2008, 30(1): 24-27, the method recorded in the literature is to estimate the actual measured voltage and current with a diagonal recursive neural network observer after coordinate transformation For current and angular velocity, the difference between the estimated value and the actual value is used to adjust the connection weight of the neural network observer until the prediction error reaches the set value; but the problem with this method is that while predicting the angular velocity, the stator current must be Prediction, using two neural network observers

Not only that, but it is further found in the research that when switching from one steady state to another, the speed estimation converges to a wrong value, which causes the actual speed to fail to converge to the given value (which is in the control The links in the system refer to figure 1 In the label 2, the identification effect is as follows Figure 10 shown)

Therefore, the neural network MRAS speed identification method can not be practically applied in the prior art

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