A bearingless motor displacement sensorless control method based on Kalman filter

By constructing a state model and observation model of the torque winding and suspension winding based on Kalman filtering for a bearingless motor without displacement sensors, and combining the electrical characteristics of the motor, high-precision estimation of rotor radial displacement is achieved. This solves the problems of insufficient applicability and robustness of existing sensorless control methods, and improves the stability and reliability of the system.

CN122371799APending Publication Date: 2026-07-10SHANGHAI UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
SHANGHAI UNIV
Filing Date
2026-04-21
Publication Date
2026-07-10

AI Technical Summary

Technical Problem

Existing sensorless control methods for bearingless permanent magnet synchronous motors have shortcomings in terms of applicable motor types, estimation accuracy, algorithm complexity, and system robustness. In particular, it is difficult to balance high accuracy and high reliability under complex working conditions.

Method used

A sensorless control method for bearingless motors based on Kalman filtering is adopted. By constructing state and observation models of torque winding and suspension winding, and combining the electrical characteristics of the motor, Kalman filtering is used for state prediction and observation to estimate the radial displacement of the rotor. Hysteresis interval and amplitude limiting are introduced during model switching to improve stability.

Benefits of technology

Without the need for additional displacement sensors, this method improves the accuracy and reliability of displacement detection, reduces hardware costs, enhances estimation robustness and system stability under complex working conditions, broadens the applicability of the method, and meets the application requirements of high speed, high precision, and high reliability.

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Abstract

This invention discloses a sensorless control method for bearingless motors based on Kalman filtering, comprising: establishing discrete state models and observation models for the torque winding and suspension winding respectively; using voltage, current, and electrical angle signals as observation inputs; and achieving online estimation of rotor radial displacement through Kalman filtering. A model switching threshold and hysteresis interval are set according to the torque load state, adaptively switching between the torque winding model and the suspension winding model. A state prediction and observation update process is executed to obtain the posterior optimal estimate of the rotor radial displacement, which is then limited and output to the suspension force control system, achieving closed-loop suspension control under sensorless conditions. This invention fully utilizes the coupling characteristics of the two types of windings and radial displacement, improving displacement estimation accuracy and system robustness, reducing dependence on high-precision displacement sensors, and is suitable for high-speed, high-precision, and high-reliability bearingless motor control scenarios.
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Citation Information

Patent Citations

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