多变运行工况下混合励磁轴向磁场永磁电机自适应模型预测电流控制OACSTPCD
Adaptive Model Predictive Current Control of Hybrid Excited Axial Field Permanent Magnet Motor Under Multiple Operating Conditions
针对多变运行工况下混合励磁轴向磁场永磁(hybrid excited axial field permanent magnet,HE-AFPM)电机中电励磁磁场与电枢磁场之间矢量耦合、端部漏磁等导致的电机参数非线性变化问题,该文提出一种混合励磁轴向磁场永磁电机自适应模型预测电流控制方法.详细分析HE-AFPM电机拓扑结构及参数变化特性;引入多矢量模型预测电流控制,理论推导分析HE-AFPM电机参数对控制系统的敏感性.在此基础上,将多矢量预测模型作为自适应控制的可调模型,降低了控制算法的复杂度和控制系统的计算负荷,提高了控制系统响应速度;依据波波夫(Popov)超稳定理论,引入3个不等式稳定判据设计各参数的自适应律,可实现多变运行工况下的HE-AFPM电机磁链、自感、互感等参数的有效识别,提高了控制模型的准确性和电流跟踪性能.最后,通过实验验证所提出的自适应模型预测电流控制的有效性.
In view of the non-linear parameter variation of the hybrid excited axial field permanent magnet(HE-AFPM)motor caused by the vector coupling effect between the excitation and armature flux,the end leakage flux effect,and etc.in multiple operating modes,an adaptive model predictive current control method is proposed in this paper.The machine topology and parameter variation characteristics of the HE-AFPM motor are analyzed in detail.The multi-vector model predictive current control is introduced,and the sensitivity of the HE-AFPM motor parameters to the control system is theoretically derived.On this basis,the multi-vector predictive model is adopted to the adjustable model of the adaptive control,which reduces the complexity of the control algorithm and the computational load of the control system.In addition,according to Popov's hyper-stability theory,three-inequalities stability criterion is introduced to design the adaptive law of each parameter.In this way,the parameters including flux linkage,self-inductance,and mutual inductance are effectively identified.And then,the accuracy of the control model and the current tracking performance are improved.Finally,the effectiveness of the proposed adaptive model predictive current control is verified by the experiments.
徐磊;刘浩;朱孝勇;张超;范文杰
江苏大学电气信息工程学院,江苏省 镇江市 212013
动力与电气工程
混合励磁轴向磁场永磁参数自适应模型预测电流控制
hybrid excitedaxial field permanent magnet(AFPM)parameter adaptivemodel predictive current control
《中国电机工程学报》 2024 (002)
725-736,中插25 / 13
国家自然科学基金项目(51907081,51937006). Project Supported by National Natural Science Foundation of China(51907081,51937006).
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