發(fā)布時間：2018-03-20 07:39

  本文選題：永磁同步電機(jī)　切入點(diǎn)：神經(jīng)網(wǎng)絡(luò)　出處：《電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,交流傳動成為了工業(yè)電氣傳動的主要研究與應(yīng)用的方向。而永磁同步電機(jī)(Permanent Magnet Synchronous Motor,PMSM)憑借著體積小、質(zhì)量輕、功率密度大、低速輸出轉(zhuǎn)矩大、效率高以及維護(hù)簡單等優(yōu)點(diǎn),一直作為電氣傳動方向理論與應(yīng)用研究的熱點(diǎn)。對于這樣一個復(fù)雜的非線性控制對象,參數(shù)動態(tài)變化和內(nèi)部狀態(tài)耦合使得傳統(tǒng)的控制方法并不能得到極佳的控制和解耦效果,因而一系列針對PMSM的智能控制研究應(yīng)運(yùn)而生。神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)控制就是其中一種可行的方案。神經(jīng)網(wǎng)絡(luò)具有以任意精度逼近非線性對象的能力,結(jié)合逆系統(tǒng)的方法對永磁同步電機(jī)實(shí)現(xiàn)解耦控制,具有良好的效果。但神經(jīng)網(wǎng)絡(luò)并行處理的特性,導(dǎo)致它并不適合在一般的處理器上實(shí)現(xiàn)�；贔PGA的神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)可以充分發(fā)揮這一特性,解決這一問題。本文在PMSM的神經(jīng)網(wǎng)絡(luò)解耦控制的理論基礎(chǔ)上,針對神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)進(jìn)行了建模與仿真,驗(yàn)證了神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)模型的可行性。得出了性能良好的神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)模型的結(jié)構(gòu)與參數(shù)。并進(jìn)一步對神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)模塊的硬件實(shí)現(xiàn)方法進(jìn)行了研究。將FPGA作為實(shí)現(xiàn)平臺,構(gòu)建了多種神經(jīng)網(wǎng)絡(luò)激勵函數(shù)模塊,比較了不同實(shí)現(xiàn)方法的特點(diǎn)。并在前面研究的基礎(chǔ)上完成神經(jīng)網(wǎng)絡(luò)FPGA模塊的建立、仿真與驗(yàn)證。在PMSM神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)的模型建立過程中,根據(jù)對PMSM數(shù)學(xué)模型的分析,進(jìn)行了永磁同步電機(jī)可逆性的相關(guān)推導(dǎo),并建立了逆系統(tǒng)模型。在此基礎(chǔ)上,依據(jù)神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)原理,建立神經(jīng)網(wǎng)絡(luò)模型,并通過Matlab訓(xùn)練得到理想的神經(jīng)網(wǎng)絡(luò)模塊的結(jié)構(gòu)與參數(shù)。將訓(xùn)練好的模塊帶回PMSM神經(jīng)網(wǎng)絡(luò)逆系統(tǒng)模型中驗(yàn)證,反映出良好的性能效果。利用Matlab和System Generator對神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)的算法進(jìn)行仿真實(shí)驗(yàn),在此過程中利用基于查找表的線性逼近方法和CORDIC算法分別設(shè)計了Sigmoid激勵函數(shù)和Gauss激勵函數(shù),并利用建立好的激勵函數(shù)完成神經(jīng)元的設(shè)計與測試。按照神經(jīng)網(wǎng)絡(luò)訓(xùn)練實(shí)驗(yàn)中得到的神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)與參數(shù)建立神經(jīng)網(wǎng)絡(luò)FPGA模型,并利用解耦系統(tǒng)仿真獲得的樣本進(jìn)行仿真與驗(yàn)證。
[Abstract]:In recent years, AC drive has become the main research and application direction of industrial electric drive, while permanent Magnet Synchronous Motor (PMSM) is a kind of permanent magnet synchronous motor with small volume, light weight, high power density and large output torque at low speed. The advantages of high efficiency and simple maintenance have always been the focus of research on the direction theory and application of electrical transmission. For such a complex nonlinear control object, Because of the dynamic change of parameters and the coupling of internal state, the traditional control method can not get the excellent control and decoupling effect. As a result, a series of intelligent control studies for PMSM have emerged. Neural network inverse system control is one of the feasible schemes. Neural network has the ability to approach nonlinear objects with arbitrary precision. The decoupling control of permanent magnet synchronous motor (PMSM) based on inverse system method has good effect. Therefore, it is not suitable to be implemented on a general processor. The neural network implementation based on FPGA can give full play to this characteristic and solve this problem. This paper is based on the theory of decoupling control of PMSM neural network. The neural network inverse system is modeled and simulated. The feasibility of the neural network inverse system model is verified. The structure and parameters of the neural network inverse system model with good performance are obtained. Furthermore, the hardware implementation method of the neural network inverse system module is studied. The FPGA is used as the implementation platform. Several kinds of neural network excitation function modules are constructed, and the characteristics of different implementation methods are compared. The establishment, simulation and verification of the neural network FPGA module are completed on the basis of the previous research. In the process of modeling the PMSM neural network inverse system, the model of the neural network inverse system is built. According to the analysis of PMSM mathematical model, the reversibility of PMSM is deduced, and the inverse system model is established. On the basis of this, the neural network model is established according to the principle of neural network inverse system. The structure and parameters of the ideal neural network module are obtained by Matlab training. The trained module is brought back to the inverse system model of PMSM neural network to verify. Matlab and System Generator are used to simulate the algorithm of neural network. In the process, the Sigmoid excitation function and Gauss excitation function are designed by using the linear approximation method based on look-up table and CORDIC algorithm, respectively. According to the structure and parameters of neural network training experiment, the neural network FPGA model is established. And the samples obtained by decoupling system simulation are simulated and verified.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM341

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 覃祥菊,朱明程,張?zhí)?魏忠義;FPGA動態(tài)可重構(gòu)技術(shù)原理及實(shí)現(xiàn)方法分析[J];電子器件;2004年02期

，

本文編號：1638113