本文選題：異步電機　切入點：無傳感器控制　出處：《中國礦業(yè)大學》2014年碩士論文　論文類型：學位論文

【摘要】：在現(xiàn)代交流調(diào)速領(lǐng)域，矢量控制技術(shù)以其性能優(yōu)良、方法簡單可靠等優(yōu)點，已經(jīng)廣泛應(yīng)用于各種交流電機的高性能控制。隨著制造技術(shù)和電力電子技術(shù)的發(fā)展，異步電機（IM）的性能和效率都得到了提升，體積卻越來越小。IM應(yīng)用更加普遍，其高性能無速度傳感器控制也受到廣泛關(guān)注。眾多無速度傳感器控制方法中，卡爾曼濾波以其動態(tài)性能好，不受參數(shù)變化影響等優(yōu)點，可以很好的實現(xiàn)IM無傳感器控制。但是，傳統(tǒng)擴展卡爾曼濾波（EKF）方法在對非線性系統(tǒng)方程進行線性化處理時，算法上存在的誤差導致精確度不夠。因而在實際應(yīng)用中，需要對傳統(tǒng)的卡爾曼濾波方法進一步改善。本文針對傳統(tǒng)EKF方法的這一缺點，對新型的卡爾曼濾波方法進行了深入研究。對其進行了仿真，驗證系統(tǒng)的估計精度明顯提高，取得了較好的效果。 首先，，對IM的常用控制方法簡單介紹。采用基于轉(zhuǎn)子磁鏈定向的方法建立IM矢量控制系統(tǒng)模型，通過仿真驗證了模型的正確性和有效性。 其次，對傳統(tǒng)擴展卡爾曼濾波方法進行了介紹，構(gòu)建電壓重構(gòu)模塊，在Matlab軟件中建立了基于EKF的IM無速度傳感器控制系統(tǒng)，并對其進行了仿真研究，證明了該方法的可行性。針對EKF方法估計精度較低的問題，對新型的卡爾曼濾波方法進行了深入的研究，即通過無跡變換（UT）實現(xiàn)無跡卡爾曼濾波（UKF）。 最后，給出基于UKF的IM無速度傳感器控制系統(tǒng)模型，通過仿真對兩種方法進行了分析，結(jié)果表明新算法能明顯提高系統(tǒng)的轉(zhuǎn)速估計效果，并改變電機參數(shù)考察UKF方法的魯棒性。
[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.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM343

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