本文選題：馬爾科夫鏈 + 多模型��； 參考：《西安理工大學(xué)》2017年碩士論文

【摘要】：轉(zhuǎn)速的閉環(huán)控制在高性能交流調(diào)速系統(tǒng)中是不可缺少的,一般通過(guò)光電編碼器等速度傳感器對(duì)轉(zhuǎn)速進(jìn)行檢測(cè)。然而,速度傳感器的安裝給系統(tǒng)帶來(lái)了很多負(fù)而影響,例如,系統(tǒng)硬件成本增加,難以適應(yīng)高溫、高濕度等惡劣環(huán)境,降低了調(diào)速系統(tǒng)的簡(jiǎn)易性和可靠性,限制了其應(yīng)用范圍。因此,近年來(lái)無(wú)速度傳感器矢量控制技術(shù)受到國(guó)內(nèi)外學(xué)者的廣泛關(guān)注,成為了電機(jī)控制領(lǐng)域的研究熱點(diǎn)。本文主要針對(duì)基于馬爾科夫鏈的多模型擴(kuò)展卡爾曼濾(Multiple-Model Extended Kalman Filter with Markov Chain, MC-MM-EKF)感應(yīng)電機(jī)轉(zhuǎn)速估計(jì)方法的魯棒性和抗差性能進(jìn)行了深入研究。第一,本文分析了感應(yīng)電機(jī)的數(shù)學(xué)模型,并基于數(shù)學(xué)模型分析了電機(jī)本身的穩(wěn)定性。第二,詳細(xì)論述了擴(kuò)展卡爾曼濾波的基本原理及其在感應(yīng)電機(jī)無(wú)速度傳感器矢量控制中的應(yīng)用;討論了干擾和電機(jī)參數(shù)變化對(duì)無(wú)速度傳感器控制性能的影響,特別是對(duì)電機(jī)轉(zhuǎn)速估計(jì)環(huán)節(jié)的影響。第三,闡述了多模型理論的基本原理,分析了多模型理論對(duì)模型不確定性問(wèn)題的解決思路;建立了基于擴(kuò)展卡爾曼濾波轉(zhuǎn)速辨識(shí)的多模型抗差數(shù)學(xué)模型,并研究了其抗差機(jī)理;寬速范圍尤其是低速條件下,分析了基于馬爾科夫鏈的多模型擴(kuò)展卡爾曼濾波無(wú)速度傳感器控制抗差系統(tǒng)的穩(wěn)定性和對(duì)參數(shù)的敏感性。第四,通過(guò)Matlab/Simulink軟件對(duì)基于馬爾科夫鏈的多模型擴(kuò)展卡爾曼濾波的感應(yīng)電機(jī)無(wú)速度傳感器矢量控制系統(tǒng)抗差性能進(jìn)行了仿真驗(yàn)證。最后,搭建了以TI公司DSP芯片TMS320F28335為微處理器的實(shí)驗(yàn)平臺(tái),并對(duì)基于馬爾科夫鏈的多模型擴(kuò)展卡爾曼濾波轉(zhuǎn)速辨識(shí)方法進(jìn)行了實(shí)驗(yàn)驗(yàn)證。仿真和實(shí)驗(yàn)結(jié)果表明與擴(kuò)展卡爾曼濾波算法相比,本文提出的轉(zhuǎn)速估計(jì)方法能有效提高系統(tǒng)模型對(duì)于實(shí)際系統(tǒng)以及外部環(huán)境變化的適應(yīng)性,顯著降低了在電機(jī)參數(shù)變化和干擾發(fā)生時(shí)的轉(zhuǎn)速估計(jì)誤差,提高了感應(yīng)電機(jī)無(wú)速度傳感器矢量控制系統(tǒng)的穩(wěn)態(tài)和動(dòng)態(tài)性能。
[Abstract]:Closed-loop speed control is indispensable in high performance AC speed regulation system. Speed is generally detected by photoelectric encoder and other speed sensors. However, the installation of speed sensors has a lot of negative effects on the system. For example, the hardware cost of the system is increased, it is difficult to adapt to the harsh environment such as high temperature and humidity, which reduces the simplicity and reliability of the speed regulating system, and limits its application scope. Therefore, in recent years, speed sensorless vector control technology has been widely concerned by scholars at home and abroad, and has become a research hotspot in the field of motor control. In this paper, the robustness and robust performance of the multi-model extended Kalman filter Multiple-Model Extended Kalman Filter with Markov Chain, MC-MM-EKF-based speed estimation method based on Markov chain are studied in detail. Firstly, the mathematical model of induction motor is analyzed, and the stability of the motor itself is analyzed based on the mathematical model. Secondly, the basic principle of extended Kalman filter and its application in speed sensorless vector control of induction motor are discussed in detail, and the influence of interference and motor parameter change on speed sensorless control performance is discussed. Especially the effect on motor speed estimation. Thirdly, the basic principle of multi-model theory is expounded, and the solution of multi-model theory to model uncertainty is analyzed, and the mathematical model of multi-model robust based on speed identification of extended Kalman filter is established, and its robust mechanism is studied. In the wide speed range, especially at low speed, the stability and sensitivity to parameters of multi-model extended Kalman filter (EKF) based on Markov chain for robust control of speed sensorless systems are analyzed. Fourthly, the robust performance of speed sensorless vector control system of induction motor based on Markov chain extended Kalman filter is simulated by Matlab/Simulink software. Finally, an experimental platform based on TI DSP chip TMS320F28335 is built, and the method of multi-model extended Kalman filter speed identification based on Markov chain is verified. The simulation and experimental results show that the proposed speed estimation method can effectively improve the adaptability of the system model to the actual system and the external environment, compared with the extended Kalman filter. The error of speed estimation is significantly reduced when the motor parameters change and disturbance occurs, and the steady-state and dynamic performance of the sensorless vector control system of induction motor is improved.
【學(xué)位授予單位】：西安理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TM346

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