基于新型卡尔曼滤波的异步电机无传感器控制系统研究

发布时间：2018-03-04 06:17

本文选题：异步电机　切入点：无传感器控制　出处：《中国矿业大学》2014年硕士论文　论文类型：学位论文

【摘要】：在现代交流调速领域，矢量控制技术以其性能优良、方法简单可靠等优点，已经广泛应用于各种交流电机的高性能控制。随着制造技术和电力电子技术的发展，异步电机（IM）的性能和效率都得到了提升，体积却越来越小。IM应用更加普遍，其高性能无速度传感器控制也受到广泛关注。众多无速度传感器控制方法中，卡尔曼滤波以其动态性能好，不受参数变化影响等优点，可以很好的实现IM无传感器控制。但是，传统扩展卡尔曼滤波（EKF）方法在对非线性系统方程进行线性化处理时，算法上存在的误差导致精确度不够。因而在实际应用中，需要对传统的卡尔曼滤波方法进一步改善。本文针对传统EKF方法的这一缺点，对新型的卡尔曼滤波方法进行了深入研究。对其进行了仿真，验证系统的估计精度明显提高，取得了较好的效果。首先，，对IM的常用控制方法简单介绍。采用基于转子磁链定向的方法建立IM矢量控制系统模型，通过仿真验证了模型的正确性和有效性。其次，对传统扩展卡尔曼滤波方法进行了介绍，构建电压重构模块，在Matlab软件中建立了基于EKF的IM无速度传感器控制系统，并对其进行了仿真研究，证明了该方法的可行性。针对EKF方法估计精度较低的问题，对新型的卡尔曼滤波方法进行了深入的研究，即通过无迹变换（UT）实现无迹卡尔曼滤波（UKF）。最后，给出基于UKF的IM无速度传感器控制系统模型，通过仿真对两种方法进行了分析，结果表明新算法能明显提高系统的转速估计效果，并改变电机参数考察UKF方法的鲁棒性。
[Abstract]:In the field of modern AC speed regulation, vector control technology has been widely used in the high performance control of various AC motors with the advantages of excellent performance, simple and reliable method, etc. With the development of manufacturing technology and power electronics technology, The performance and efficiency of Induction Motor (IMM) have been improved, but the volume is smaller and smaller. IM is more and more widely used, and its high performance sensorless speed control has been paid more attention. Among the many speed sensorless control methods, Kalman filter has the advantages of good dynamic performance and no influence of parameters. However, the traditional extended Kalman filter (EKF) method is used to linearize the nonlinear system equations. The error in the algorithm leads to inaccuracy. Therefore, the traditional Kalman filtering method needs to be further improved in practical application. This paper aims at the shortcoming of the traditional EKF method. The new Kalman filtering method is studied, and the simulation results show that the estimation accuracy of the system has been improved obviously and good results have been obtained. Firstly, the common control methods of IM are briefly introduced. The model of IM vector control system based on rotor flux orientation is established, and the correctness and validity of the model are verified by simulation. Secondly, the traditional extended Kalman filter method is introduced, the voltage reconstruction module is constructed, and the IM sensorless control system based on EKF is established in the Matlab software, and the simulation is carried out. The feasibility of this method is proved. Aiming at the problem of low estimation accuracy of EKF method, a new Kalman filtering method is studied, that is, unscented Kalman filter is realized by unscented transform. Finally, the model of IM sensorless control system based on UKF is given. Two methods are analyzed by simulation. The results show that the new algorithm can obviously improve the speed estimation effect of the system. The robustness of the UKF method is investigated by changing the motor parameters.
【学位授予单位】：中国矿业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM343

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