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Method for estimating rotating speed of brushless direct current motor

A brush DC motor and speed estimation technology, which is applied in the control of generators, motor generator control, electronic commutation motor control, etc., can solve the problems of insufficient utilization of feedback information, poor local search ability of particle swarm algorithm, etc.

Inactive Publication Date: 2015-10-21
CHONGQING UNIV
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Problems solved by technology

Using particle swarm optimization to optimize the noise matrix can improve the accuracy of speed estimation results, but the particle swarm algorithm has poor local search ability in the later stage, and the feedback information is not fully utilized.

Method used

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  • Method for estimating rotating speed of brushless direct current motor
  • Method for estimating rotating speed of brushless direct current motor
  • Method for estimating rotating speed of brushless direct current motor

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Embodiment Construction

[0053] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments, and this implementation does not limit the present invention.

[0054] The brushless DC motor speed estimation method mainly includes the extended Kalman filter1, the objective function2 and the ant colony particle swarm algorithm3. Its structure is as figure 1 As shown: Firstly, the extended Kalman filter 1 is used to estimate the rotational speed, and the posterior rotational speed estimation value is obtained. According to the objective function 2 and the filter parameter value, the ant colony particle swarm optimization algorithm 3 is used to optimize Q and R, and the obtained correction The value is used as the input parameter of the extended Kalman filter 1 for parameter estimation until an optimized rotational speed estimate is obtained.

[0055] The square wave brushless DC motor model established in the static abc coordinate system, th...

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Abstract

The invention discloses a method for estimating the rotating speed of a brushless direct current motor. In allusion to a problem that a Kalman filter system noise matrix and a measurement noise matrix are difficult to be acquired in estimation of a Kalman filter for the rotating speed of the brushless direct current motor, the invention provides a method in which the system noise matrix and the measurement noise matrix are optimized at first by using an ant colony algorithm and a particle swarm algorithm, and then estimation for the rotating speed is carried out by using an extended Kalman filter. The algorithm integrates advantages of the ant colony algorithm and the particle swarm algorithm, and the particle swarm algorithm is introduced into the ant colony algorithm in allusion to defects that a prematurity phenomenon easily occurs in the ant colony algorithm and that the particle swarm algorithm is poor in local searching ability in a later period, thereby enabling ant colony algorithm to have characteristics of the particle swarm algorithm. The optimized Kalman filter is applied to estimation of the rotating speed of the brushless direct current motor, thereby being capable of improving the precision in estimation for the rotating speed of the brushless direct current motor.

Description

technical field [0001] The invention belongs to the field of motor control methods and relates to a method for estimating the rotational speed of a brushless DC motor. Background technique [0002] The motor speed is mainly obtained by two methods: physical sensor detection and software algorithm. Using physical sensors to detect will increase the fault point and physical cost, while using software algorithms to identify the speed can overcome the shortcomings of hardware methods such as many faults, large size, and difficult maintenance. Therefore, the research on virtual speed sensor using software algorithm to measure speed has become an important research direction of modern transmission control technology, and Kalman filter is considered to be an important method of speed estimation in virtual speed sensor. [0003] One of the main problems in using the extended Kalman filter for speed estimation is the selection of the noise matrix. The accuracy and convergence of the...

Claims

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Application Information

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IPC IPC(8): H02P21/14H02P21/13
Inventor 盛朝强谢昭莉黄凯陈超
Owner CHONGQING UNIV
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