A method and apparatus for controlling a motor

By acquiring motor parameters based on the prediction period and updating them according to real-time operating conditions in the finite control set model predictive control algorithm, and combining the recursive least squares method with forgetting factor and the motor stator voltage equation, the problem of insufficient accuracy and response caused by parameter drift in FOC motor control is solved, and more efficient motor control is achieved.

CN122419293APending Publication Date: 2026-07-17SHAANXI HANDE AXLE CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHAANXI HANDE AXLE CO LTD
Filing Date
2026-04-13
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Existing field-oriented control (FOC) motor control methods suffer from decreased control accuracy and insufficient dynamic response due to parameter drift, making it difficult to meet the requirements for high precision and high response.

Method used

In the finite control set model predictive control algorithm, motor parameters and identification parameters are obtained based on the prediction period and updated according to real-time operating conditions. By combining the recursive least squares method with forgetting factor and the motor stator voltage equation, the identification parameters are dynamically adjusted to ensure that the model fits the actual state.

Benefits of technology

It improves the accuracy and response speed of motor control, reduces model mismatch problems caused by parameter drift, and enhances the stability and efficiency of the system.

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Abstract

The application provides a motor control method and device, and the method comprises the following steps: obtaining motor parameters and identification parameters of a motor based on a prediction period, wherein at least part of the identification parameters can be updated based on real-time working conditions of the motor, and the update period is an integer multiple of the prediction period, the motor parameters comprise q-axis current, d-axis current and electric angular velocity, and the identification parameters comprise stator resistance, q-axis inductance, d-axis inductance and permanent magnet flux linkage; and control parameters of the motor are determined according to the motor parameters, the identification parameters and a limited control set model predictive control algorithm, so as to control the operation of the motor. Thus, by setting the identification parameters and updating the identification parameters based on the real-time working conditions, the model can be made to be close to the actual operation state of the motor in real time, so that the model mismatch problem caused by parameter drift can be effectively weakened, and the control precision of the limited control set model predictive control algorithm is improved.
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