LS-SVM-based sensorless control system of bearingless induction motor

Abstract

The invention provides an LS-SVM-based sensorless control system of a bearingless induction motor. According to the non-linear estimation model of radial displacement variable, formulas (4) and (5) are given. The generalization ability of SVM is used to approximate the nonlinear relationship between radial displacement and related physical variables, so that the real-time estimation of the radialdisplacement of the rotor can be realized, the estimation accuracy of the radial displacement does not depend on the accurate mathematical model of the motor, the radial displacement estimation errorcaused by the inaccurate motor parameters can be effectively overcome, and the complicated signal extraction algorithm is not needed, and the LS-SVM rotor radial displacement estimator is constructed,thereby getting rid of that mechanical radial displacement sensor, getting rid of the mechanical radial displacement sensor, overcoming the influence of modeling error of nonlinear radial displacement estimation model, and avoiding the radial displacement estimation error caused by inaccurate motor parameters. A new control system is constructed based on LS-SVM rotor radial displacement estimator, which can effectively reduce the cost of bearingless induction motor control system.

Description

technical field [0001] The invention relates to the technical field of parameter detection of novel special AC motors, in particular to a bearingless asynchronous motor without radial displacement sensor control system based on LS-SVM. Background technique [0002] Bearingless motor is based on the similarity between magnetic bearing and AC motor stator structure. It is a new type of motor suitable for high-speed operation developed in recent years. It has broad application prospects in aerospace, material sealing transmission, advanced manufacturing and other fields. In order to realize the stable magnetic levitation control of the rotor, it is necessary to detect and control the radial displacement of the rotor in real time; the eddy current displacement sensor is extremely expensive, and the self-sensor detection of the radial displacement of the rotor is to reduce the cost of its control system and promote its practical application The essential. [0003] A search of ex...

AI Technical Summary

Problems solved by technology

Among the currently researched displacement self-sensing detection methods, the observer method is highly dependent on the mathematical model of the bearingless motor and has poor robustness; the high-frequency injection method requires high-frequency signal injection and complex signal extraction, which is not convenient for technical implementation; based on The displacement detection method of intelligent theory has been initially explored, but it cannot completely get rid of the dependence on the mechanical radial displacement sensor in operation

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