基于参数识别的PMSM电流环自适应非奇异终端滑模控制方法

By introducing an adaptive non-singular terminal sliding mode control method based on parameter identification into a permanent magnet synchronous motor, the chattering and overshoot problems of traditional sliding mode control under parameter perturbation and external disturbances are solved, and efficient and stable control of the motor is achieved.

CN122159748BActive Publication Date: 2026-07-17TIANJIN UNIV OF TECH & EDUCATION (TEACHER DEV CENT OF CHINA VOCATIONAL TRAINING & GUIDANCE)

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
TIANJIN UNIV OF TECH & EDUCATION (TEACHER DEV CENT OF CHINA VOCATIONAL TRAINING & GUIDANCE)
Filing Date
2026-05-07
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional sliding mode control methods are difficult to simultaneously meet the requirements of chatter suppression and convergence speed under parameter perturbation and external disturbance in permanent magnet synchronous motors, and stator current waveform distortion and torque pulsation affect the system control accuracy and stability.

Method used

An adaptive non-singular terminal sliding mode control (ANTSMC) method based on parameter identification is adopted. By establishing a current loop model in the dq axis coordinate system and combining it with an Adaline neural network to identify motor parameters, an ANTSMC is constructed to achieve online compensation and robust control of motor parameters.

Benefits of technology

It effectively suppresses chattering, reduces overshoot, improves the robustness of the controller to parameter perturbations and external disturbances, enhances control accuracy and stability, and ensures efficient operation of the motor.

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Abstract

本发明公开了一种基于参数识别的PMSM电流环自适应非奇异终端滑模控制方法,包括:步骤1,基于实际控制系统电机参数发生变动或扰动建立d‑q轴坐标系下的PMSM的电流环模型,得到q轴定子电流的一阶微分和d轴定子电流的一阶微分;步骤2,定义电流环误差构建ANTSMC,ANTSMC包括自适应趋近律和非奇异终端滑模面,对两者求导并联立得到q轴输出电压和d轴输出电压,通过ANTSMC对永磁同步电机磁场矢量控制的电流环进行控制,通过李雅普诺夫稳定判据证明ANTSMC的系统稳定性;步骤3,利用基于Adaline神经网络的参数识别器对PMSM的电流环模型的电机实际参数进行识别后应用到ANTSMC。
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