五相永磁同步电动机无模型预测容错控制OA
Model-free predictive fault-tolerant control of five-phase PMSM
针对开路故障下运行时五相永磁同步电动机参数易发生摄动,严重影响模型预测电流控制性能的问题,提出一种新的无模型预测容错控制策略.在同步旋转坐标系中建立五相永磁同步电动机相开路故障情况下的模型,分析由参数摄动及未建模动态引起的总扰动.基于超局部模型,在基波和三次谐波平面中设计扩展状态观测器估计扰动,并将其补偿到基于无差拍控制得到的参考电压中.在MATLAB/Simulink中搭建基于五相PMSM的MF-PFTC模型,并与传统MPFTC进行性能对比仿真分析.建立五相PMSM试验平台,分别对传统MPFTC策略和所提出MF-PFTC策略进行相关试验.结果表明:所提出方法在正常和开路故障情况下均具备优良的稳态、动态和鲁棒性能,验证了所提出策略的有效性.
To solve the performance degradation of model predictive current control caused by parameter mismatch in five-phase permanent magnet synchronous motor(PMSM)under open-circuit fault,the novel model-free predictive fault-tolerant control strategy was proposed.The five-phase PMSM model was built in the rotary synchronous frame under open-circuit fault condition,and the total disturbance caused by parameter uncertainties and unmodeled dynamics was analyzed.Based on the ultra-local model,the extended state observer was designed to estimate the disturbance in the fundamental and the third harmonic subspaces.The disturbance was employed as compensation of the voltage references which were obtained on the basis of deadbeat control.The proposed MPFTC model for five-phase PMSM was built in MATLAB/Simulink,and the performance comparison simulation analysis with the traditional MPFTC was conducted.The five-phase PMSM experimental platform was established,and the relevant experiments were carried out by the traditional MPFTC strategy and the proposed MF-PFTC strategy,respectively.The results show that by the proposed method,excellent steady-state,dynamic and robust performance are achieved under both healthy and open-circuit fault conditions,and the proposed strategy is verified.
周华伟;朱流川;周振伍;颜黎浩
江苏大学电气信息工程学院,江苏镇江 212013江苏大学电气信息工程学院,江苏镇江 212013江苏大学电气信息工程学院,江苏镇江 212013炫杏新电子科技(苏州)有限公司,江苏苏州 215316
信息技术与安全科学
永磁同步电动机无模型预测控制容错控制开路故障参数失配超局部模型扩展状态观测器
permanent magnet synchronous motormodel-free predictive controlfault-tolerant controlopen-circuit faultparameter mismatchultra-local modelextended state observer
《江苏大学学报(自然科学版)》 2026 (1)
71-78,8
国家自然科学基金资助项目(52277052)
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