基于模糊动态代价函数的永磁同步电机有限控制集模型预测电流控制
发布时间：2019-03-31 09:59
【摘要】：提出一种基于模糊动态代价函数的有限控制集模型预测电流控制方法。分析了dq轴电流及开关次数三个控制目标权重分配不同对电流控制性能的影响,针对传统有限控制集模型预测电流控制中代价函数的dq轴电流项无针对性优化的状况,通过判断转速偏差及转速变化率,应用模糊算法对权重系数进行多目标动态优化分配,并给出模糊论域和相应的模糊推理规则设计。该方法提高了动态过程中系统电流响应速度,优化了逆变器开关频率,改善了不同权重系数下系统动态性能和稳态裕度相互制约的状况。仿真和实验结果均证明了所提方法的有效性。
[Abstract]:A finite control set model predictive current control method based on fuzzy dynamic cost function is proposed. The influence of dq axis current and switching number on the current control performance is analyzed. The current term of dq axis in the traditional finite control set model prediction is not optimized in the case of the dq axis current term in the traditional finite control set model predictive current control, and the current term of the dq axis is not optimized according to the traditional finite control set model. By judging the speed deviation and the speed change rate, the fuzzy algorithm is used to optimize the weight coefficient dynamically, and the fuzzy universe and the corresponding fuzzy inference rules are given. This method improves the current response speed of the system, optimizes the switching frequency of the inverter, and improves the condition that the dynamic performance and the steady-state margin of the system are restricted by each other under different weight coefficients. Simulation and experimental results demonstrate the effectiveness of the proposed method.
【作者单位】： 西北工业大学自动化学院;
【基金】：国家自然基金面上项目资助(51177135)
【分类号】：TM341
，
本文编号：2450805
[Abstract]:A finite control set model predictive current control method based on fuzzy dynamic cost function is proposed. The influence of dq axis current and switching number on the current control performance is analyzed. The current term of dq axis in the traditional finite control set model prediction is not optimized in the case of the dq axis current term in the traditional finite control set model predictive current control, and the current term of the dq axis is not optimized according to the traditional finite control set model. By judging the speed deviation and the speed change rate, the fuzzy algorithm is used to optimize the weight coefficient dynamically, and the fuzzy universe and the corresponding fuzzy inference rules are given. This method improves the current response speed of the system, optimizes the switching frequency of the inverter, and improves the condition that the dynamic performance and the steady-state margin of the system are restricted by each other under different weight coefficients. Simulation and experimental results demonstrate the effectiveness of the proposed method.
【作者单位】： 西北工业大学自动化学院;
【基金】：国家自然基金面上项目资助(51177135)
【分类号】：TM341
，
本文编号：2450805
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