A Model-Free Predictive Repetitive Control Method and System for Dual Three-Phase Permanent Magnet Synchronous Motors Based on Dual Subspace Virtual Vectors

By employing a model-free predictive repetitive control method with dual subspace virtual vectors in a dual three-phase permanent magnet synchronous motor, the problems of strong parameter dependence and insufficient harmonic compensation are solved, independent control of the fundamental wave and harmonics is achieved, and the robustness and harmonic suppression effect of the system are improved.

CN122001254BActive Publication Date: 2026-06-30ZHEJIANG UNIV OF TECH
View PDF 2 Cites 0 Cited by

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
ZHEJIANG UNIV OF TECH
Filing Date
2026-04-10
Publication Date
2026-06-30

AI Technical Summary

Technical Problem

Existing model-free prediction methods for dual three-phase permanent magnet synchronous motors suffer from problems such as strong parameter dependence, high computational burden, significant noise impact, and insufficient harmonic compensation. In particular, under asymmetry and inverter dead-zone effects, it is difficult to effectively suppress low-order xy current harmonics.

Method used

A model-free predictive repetitive control method based on dual-subspace virtual vectors is adopted. The motor vector is decomposed into the fundamental subspace and harmonic subspace through spatial vector decoupling transformation. Independent hyperlocal models and observers are constructed respectively, and independent virtual voltage vector sets are synthesized to achieve independent control of the fundamental and harmonic waves. The repetitive controller is used to suppress periodic disturbances.

Benefits of technology

This study achieves efficient harmonic suppression and robust control of dual three-phase permanent magnet synchronous motors, reduces dependence on motor parameters, and improves system stability and control accuracy.

✦ Generated by Eureka AI based on patent content.

Smart Images

  • Figure CN122001254B_ABST
    Figure CN122001254B_ABST
Patent Text Reader

Abstract

This invention relates to a model-free predictive repetitive control method and system for a dual-subspace virtual vector dual-three-phase permanent magnet synchronous motor. Based on the acquired operating parameters, the motor's vectors are decomposed into mutually orthogonal fundamental and harmonic subspaces, and independent hyperlocal models are constructed in both subspaces. A linear extended state observer is constructed in the fundamental subspace, and a repetitive extended state observer based on repetitive control is constructed in the harmonic subspace. Independent virtual voltage vector sets and decoupled virtual voltage vector sets are synthesized in the two subspaces, respectively. Reference voltage vectors for the two subspaces are calculated, and the optimal virtual voltage vector and optimal decoupled virtual voltage vector are selected. The optimal duty cycle is calculated for each subspace, and control signals are synthesized and applied to each arm of the inverter to drive the dual-three-phase permanent magnet synchronous motor. This invention achieves model-free control in a dual-subspace manner, and the control algorithm for the motor system's model parameters exhibits better robustness in the event of parameter mismatch.
Need to check novelty before this filing date? Find Prior Art

Citation Information

Patent Citations

  • Double-three-phase permanent magnet synchronous generator double-sub-space duty ratio model predictive current control method

    CN113992093A

  • Six-phase permanent magnet synchronous motor steer-by-wire system road feeling robust control method

    CN117227830A