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机构地区:[1]合肥工业大学,合肥230009
出 处:《微特电机》2016年第10期50-53,共4页Small & Special Electrical Machines
基 金:国家自然科学基金项目(51377041)
摘 要:对于电动汽车PMSM驱动系统而言,复杂多变的负载工况和运行温度使电机参数存在较大的不确定性,直接影响基于模型的PMSM电流预测控制系统性能。论文创新性地将无模型控制和预测控制相结合,基于系统的输入和输出数据建立PMSM超局部模型,再设计PMSM驱动系统的无模型电流预测控制器,架构无模型电流预测控制的PMSM驱动系统。最后,通过系统建模和仿真研究,评价所建议的PMSM驱动系统动静态性能及其对参数变化的鲁棒性并给出结论。For PMSM drive system in electrical vehicles ( EVs), complex and changeable load conditions and operating temperature lead to the existence of large PMSM parameter uncertainty, which will directly affect the performance of conventional model-based current predictive controlled PMSM drive system. Therefore, a novel control method combining the model-free control and predictive control is proposed in this paper, the uhra-loeal model is firstly set up by only using the input and output data of PMSM drive system, then, a model-free current predictive controller is designed, and then, the model-free current predictive controlled PMSM drive system is architected. Finally, via the system modeling and numerical simulation, the dynamic and static performance and robustness against parameter variations for proposed PMSM drive system are tested and analyzed and the some conclusions are shown.
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