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Robust Predictive Current Control for Fault-Tolerant Operation of Five-Phase PM Motors Based on Online Stator Inductance Identification

机译:基于在线定子电感识别的五相PM电动机容易容忍操作的鲁棒预测电流控制

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摘要

The parameter mismatch in model predictive control (MPC), especially in fault-tolerant control, will lead to degradation of output performance. This article presents a new robust predictive current control based on online d-q inductance identification for a five-phase permanent-magnet (PM) motor under open-circuit fault. First, the MPC of the five-phase motor under the fault-tolerant operation of A-phase open-circuit is built, and then, the parameter mismatch caused by the d-q-axis inductance and the stator resistance is analyzed and discussed. Afterward, the real-time d-q-axis inductance identified by a fault-tolerance model reference adaptive system is used to replace the original parameters in MPC. Moreover, the flux linkage parameter mismatch can be rejected by the incremental predictive model in the proposed method. Therefore, the proposed method can eliminate almost all parameter mismatches, thereby improving output performance and enhancing parameter robustness under open-circuit fault. Finally, the validity and effectiveness of the proposed method are verified by experiments.
机译:模型预测控制(MPC)中的参数不匹配,特别是在容错控制中,将导致输出性能的降低。本文提出了一种基于开路故障下的五相永磁电机(PM)电机的在线D-Q电感识别新的强大预测电流控制。首先,在构建在相位开路电路的容错操作下五相电动机的MPC,然后分析并讨论由D-Q轴电感和定子电阻引起的参数失配。之后,通过容错模型参考自适应系统识别的实时D-Q轴电感用于替换MPC中的原始参数。此外,在所提出的方法中可以通过增量预测模型拒绝磁通连杆参数错配。因此,所提出的方法可以消除几乎所有参数不匹配,从而提高了开路故障下的输出性能和增强参数鲁棒性。最后,通过实验验证了所提出的方法的有效性和有效性。

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