本文選題：MSMA + 傳感器��； 參考：《沈陽理工大學(xué)》2017年碩士論文

【摘要】：磁控形狀記憶合金(Magnetically Controlled Shape Memory Alloy簡稱MSMA)是近年來出現(xiàn)的一種新型智能材料,具有形變大、響應(yīng)速度快、能量密度高等優(yōu)點,在傳感器領(lǐng)域擁有廣闊的應(yīng)用前景。變形的磁控形狀記憶合金在外力作用下由于內(nèi)部發(fā)生孿晶再取向使材料的磁化強度發(fā)生改變,通常稱這種特性為MSMA逆效應(yīng)。利用該特性可以研制MSMA傳感器。本文基于磁控形狀記憶合金的逆效應(yīng),建立磁控形狀記憶合金傳感器外特性模型,通過無跡卡爾曼濾波算法對磁控形狀記憶合金傳感器外特性模型的系數(shù)進行參數(shù)辨識,基于Matlab、Labview混合變成設(shè)計思想構(gòu)建磁控形狀記憶合金傳感器特性仿真平臺,通過該平臺可以模擬不同輸入條件(激振力、預(yù)壓力、偏置磁場)協(xié)同作用下,獲取磁控形狀記憶合金傳感器感應(yīng)電壓信號輸出,同時也可以將該特性仿真平臺與實驗系統(tǒng)相結(jié)合,深化材料的實驗研究進程。首先,研究MSMA的晶體結(jié)構(gòu)、變形機理,而后基于材料力學(xué)、動力學(xué)、電磁學(xué)等理論建立MSMA傳感器外特性模型并確立MSMA傳感器感應(yīng)電壓與外部磁場、預(yù)壓力、激振力的幅值和頻率之間的函數(shù)關(guān)系式,該模型即為參數(shù)辨識的目標(biāo)模型。其次,基于無跡卡爾曼濾波算法對傳感器外特性模型進行參數(shù)辨識,以滿足不同實驗條件下模型的適用性。將無跡卡爾曼濾波算法與混沌-量子粒子群算法分別對MSMA傳感器外特性模型參數(shù)辨識的結(jié)果進行比較分析,驗證了無跡卡爾曼濾波算法在MSMA傳感器外特性模型參數(shù)辨識方面的高效性以及準(zhǔn)確性。最后,構(gòu)建磁控形狀記憶合金傳感器特性仿真平臺并搭建MSMA傳感器實驗系統(tǒng),將實驗結(jié)果與特性仿真平臺輸出結(jié)果進行比較,二者輸出結(jié)果吻合。由磁控形狀記憶合金傳感器特性仿真平臺進行感應(yīng)電壓信號預(yù)測,指導(dǎo)后續(xù)實驗研究。
[Abstract]:Magnetically shaped memory alloy (Controlled Shape Memory Alloy) is a new type of smart material, which has the advantages of large deformation, fast response and high energy density. It has a broad application prospect in sensor field. The magnetization of the deformed magnetically controlled shape memory alloy changed due to the twin reorientation of the alloy under external force. This property is usually called MSMA inverse effect. Using this characteristic, MSMA sensor can be developed. Based on the inverse effect of magnetic control shape memory alloy, the external characteristic model of magnetic control shape memory alloy sensor is established in this paper. The parameters of the external characteristic model of magnetic control shape memory alloy sensor are identified by unscented Kalman filter algorithm. Based on the mixed design idea of Matlab / LabVIEW, the simulation platform for the characteristics of magnetically controlled shape memory alloy sensors is constructed. The platform can be used to simulate the synergistic action of different input conditions (excitation force, prepressure, bias magnetic field). The output of inductive voltage signal of magnetically controlled shape memory alloy sensor can be obtained. At the same time, the simulation platform of this characteristic can be combined with the experimental system to deepen the experimental research process. Firstly, the crystal structure and deformation mechanism of MSMA are studied. Then, based on the theories of material mechanics, dynamics and electromagnetism, the external characteristic model of MSMA sensor is established, and the inductive voltage, external magnetic field and prepressure of MSMA sensor are established. The function relation between amplitude and frequency of excitation force is the target model of parameter identification. Secondly, based on the unscented Kalman filter algorithm, the parameters of the sensor external characteristic model are identified to satisfy the applicability of the model under different experimental conditions. Unscented Kalman filter algorithm and chaos quantum particle swarm optimization algorithm are used to compare and analyze the parameter identification results of the external characteristic model of MSMA sensor. The efficiency and accuracy of the unscented Kalman filter algorithm in the parameter identification of the external characteristic model of MSMA sensor are verified. Finally, the simulation platform of magnetically controlled shape memory alloy sensor and the experimental system of MSMA sensor are constructed. The experimental results are compared with the output results of the characteristic simulation platform, and the two outputs are in good agreement with each other. Based on the simulation platform of magnetic control shape memory alloy sensor, the inductive voltage signal is predicted to guide the subsequent experimental research.
【學(xué)位授予單位】：沈陽理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212

【參考文獻】

相關(guān)期刊論文 前10條

1 王振樹;卞紹潤;劉曉宇;于凱;石云鵬;;基于混沌與量子粒子群算法相結(jié)合的負(fù)荷模型參數(shù)辨識研究[J];電工技術(shù)學(xué)報;2014年12期

2 魯軍;李敏;王鳳翔;;基于MSMA逆特性的振動傳感器理論及實驗[J];電工技術(shù)學(xué)報;2014年05期

3 魯軍;楊寬;王鳳翔;;磁控形狀記憶合金振動傳感器模型及實驗特性[J];電機與控制學(xué)報;2014年03期

4 徐小兵;鄧荊江;;形狀記憶合金驅(qū)動器的研究現(xiàn)狀及展望[J];機械研究與應(yīng)用;2013年06期

5 蔣成保;王敬民;徐惠彬;;磁性形狀記憶合金研究進展[J];中國材料進展;2011年09期

6 杜秀麗;朱玉萍;;兩軸向磁場時磁控形狀記憶合金的本構(gòu)模型[J];太原科技大學(xué)學(xué)報;2011年03期

7 翁光遠(yuǎn);王社良;王占鋒;;磁控形狀記憶合金在結(jié)構(gòu)振動控制中的應(yīng)用研究[J];西安建筑科技大學(xué)學(xué)報(自然科學(xué)版);2010年05期

8 張晶;張慶新;楊明;;磁控形狀記憶合金動態(tài)建模及仿真研究[J];沈陽航空工業(yè)學(xué)院學(xué)報;2009年04期

9 魯軍;王晨;;磁控形狀記憶合金自感知執(zhí)行器初探[J];沈陽理工大學(xué)學(xué)報;2008年04期

10 羅諶持;張明;;基于Sigma點卡爾曼濾波器的電力頻率跟蹤新算法[J];電力系統(tǒng)自動化;2008年13期

相關(guān)博士學(xué)位論文 前1條

1 張慶新;磁控形狀記憶合金特性及其執(zhí)行器應(yīng)用基礎(chǔ)研究[D];沈陽工業(yè)大學(xué);2006年

相關(guān)碩士學(xué)位論文 前2條

1 張晶;磁控形狀記憶合金微位移執(zhí)行器模型的研究[D];沈陽航空工業(yè)學(xué)院;2010年

2 仲衛(wèi)進;基于擴展卡爾曼濾波的動態(tài)負(fù)荷建模與參數(shù)辨識[D];上海交通大學(xué);2007年

，

本文編號：1810624