Parameter optimization method for brushless direct current motor controller based on grey wolf optimization

Abstract

The invention discloses a parameter optimization method for a brushless direct current motor controller based on grey wolf optimization, which comprises the steps of establishing a brushless direct current motor counter electromotive force observer, and adding a linear error function into the observer structure to construct a new observer structure combining a linear error function term and a nonlinear error feedback term; introducing a grey wolf optimization (GWO) algorithm to carry out optimization and evaluation on gains K1 and K2 of the new observer and an adjustable parameter c in the linear error function term; and establishing an error integral criterion ITAE according to an error value of the expected output speed and the actual output speed of the motor, and determining an algorithm optimization effect by taking the ITAE as an objective function of the grey wolf algorithm optimization. Compared with the traditional brushless direct current motor counter electromotive force observer, the method accelerates the convergence rate of the estimated state quantity by modifying the traditional structure of the observer; and meanwhile, the rapidity of observer error convergence andthe minimization of buffeting of an estimated signal are further ensured by introducing the grey wolf algorithm to optimize parameters, and the problem of high switching gain in a low-speed range canbe reduced to the maximum extent.

Description

technical field [0001] The invention relates to the field of sensorless control of brushless direct current motors, in particular to a parameter optimization method of a brushless direct current motor controller based on gray wolf optimization. Background technique [0002] Compared with traditional AC and DC motors, permanent magnet brushless DC motors have the characteristics of high power density, high efficiency, large torque, low loss, and low cost. They have been used in some high-performance drives such as aerospace, medical machinery and other fields. Wide range of applications. Traditional brushless DC motor controllers usually use mechanical sensors to obtain rotor position information for commutation control. However, with the continuous improvement of the requirements for the accuracy, response speed and stability of the system in various industrial control fields and the continuous deterioration of the motor operating environment, the traditional controller has...

AI Technical Summary

Problems solved by technology

This method obtains the commutation point of the rotor by detecting the zero-crossing point of the motor’s back EMF and shifting the phase by π / 6. However, due to the complexity of the motor’s working environment and electromagnetic interference and other factors, the commutation point cannot be accurately judged, and when the motor is running at a low speed, The counter electromotive force is weak, and it is difficult to detect and capture its zero crossing point

In response to the above problems, scholars proposed the back-EMF observer method. By measuring the three-phase terminal voltage and line current to construct a line back-EMF observer to estimate a series of motor information and obtain the rotor commutation point, it solved the above-mentioned zero-crossing point that is difficult to be accurate. measurement problem

However, the back electromotive force estimated by the traditional method contains a large number of high-frequency interference components, and when constructing the observer, it is necessary to select K based on the pole configuration method 1 and K 2 Two gain values. The maximum value of the gain parameter selected by this method is limited due to the amplification of noise. When the motor is running at full speed, it is very easy to jitter the estimated signal, resulting in errors in the estimated value and other problems.

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