Multi-model least square support vector machine (LSSVM) modeling method of brushless direct current motor

Abstract

The invention discloses a multi-model least square support vector machine (LSSVM) modeling method of a bearingless brushless direct current motor. According to the data driving principle, proper signals are utilized for exciting the bearingless brushless direct current motor, input and output samples are obtained, the samples are clustered according to the input set and the output set through the affinity propagation clustering, subclasses subjected to secondary clustering are subjected to the least square support vector machine fitting, a local LSSVM module is built, and further, the weighting and the form are adopted for constructing a system model of the bearingless brushless direct current motor. The multi-model LSSVM modeling method does not depend on the system mechanism and the specific parameters, a bearingless brushless direct current motor system is resolved, the modeling difficulty is lowered, and the modeling precision is improved.

Description

technical field [0001] The invention relates to a multi-model least square support vector machine modeling method for a bearingless brushless DC motor, which is suitable for the technical field of electric drive control. Background technique [0002] Brushless DC motors have excellent speed regulation performance, high efficiency, easy control, simple structure, reliable operation, and convenient maintenance, and have been widely used in the industrial field. However, traditional mechanical bearings will cause problems such as motor vibration, noise, wear, heat generation, and short life, which seriously affect the high-speed and ultra-high-speed reliable operation of the motor. In order to solve the problems caused by traditional mechanical bearings, the bearingless technology developed on the basis of magnetic suspension bearings uses the similarity between magnetic suspension bearings and motor structures to put the magnetic suspension bearing windings that generate levit...

AI Technical Summary

Problems solved by technology

However, these clustering algorithms generally have the problems that the number of clusters needs to be given in advance, the accuracy depends on the data distribution, and the convergence speed is slow; moreover, the traditional clustering methods only use the input part of the sample data, due to the inconsistency and incompleteness of the data , which is not conducive to the improvement of model accuracy

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