【摘要】：電動汽車具有清潔無污染、能量來源多樣化、能量效率高的特點,又便于實現(xiàn)智能化的管理,可以解決燃油汽車帶來的能量、環(huán)境以及交通等一系列問題,因此電動汽車已成為世界各國的研究熱點。我國電動汽車技術(shù)還處在起步階段,各方面的關(guān)鍵技術(shù)都需要研究解決。電機及其控制技術(shù)是電動汽車的關(guān)鍵技術(shù)之一,是研究開發(fā)電動汽車首先需要解決的問題。在本文中,研究對象是無刷直流電機,并對控制系統(tǒng)進(jìn)行設(shè)計,使電機運行平穩(wěn),減小其波動和噪聲。論文首先對直流無刷電機的基本結(jié)構(gòu)進(jìn)行分析,根據(jù)數(shù)學(xué)建模,并結(jié)合電動汽車用電機的應(yīng)用要求和使用范圍,在MATLAB/Simulink仿真平臺中,創(chuàng)建了仿真模型,并將先進(jìn)的PID算法應(yīng)用到建立的仿真模型之中。最終通過實驗驗證該先進(jìn)的PID算法可以有效的提高系統(tǒng)的穩(wěn)定性、與魯棒性。在完成論文的基礎(chǔ)上。本課題的主要工作和結(jié)論如下:(1)利用MATLAB對徑向基神經(jīng)網(wǎng)絡(luò)PID在無刷直流電機控制中的應(yīng)用進(jìn)行仿真研究,證明了徑向基神經(jīng)網(wǎng)絡(luò)PID比簡單的PID控制性能更優(yōu)越,在仿真結(jié)果中,明顯的顯示出,在動態(tài)指標(biāo)方面,其超調(diào)量明顯減小、震蕩次數(shù)、調(diào)節(jié)時間、上升時間縮短。在穩(wěn)態(tài)性能方面,其調(diào)節(jié)時間變化并不明顯。從總體上證明了徑向基神經(jīng)網(wǎng)絡(luò)PID比簡單的PID控制性能更優(yōu)越。(2)采用飛思卡爾的MC56F801X系列芯片作為主控電路的核心、并且在驅(qū)動電路中采用DRV8301芯片,組成無刷直流電機控制系統(tǒng)的硬件基礎(chǔ),在軟件方面采用平臺CCS,編制系統(tǒng)的主程序以及中斷程序,最后通過軟件硬件結(jié)合,驗證了前述方法的有效性。(3)經(jīng)過實驗驗證,本文提出的無刷直流電機控制策略正確、可行。該控制系統(tǒng)硬件簡單、提高了系統(tǒng)的可靠性、魯棒性,具有良好的控制效果。論文所做的工作為無刷直流電機的廣泛應(yīng)用提供了一定的理論參考依據(jù)和工程實際方法。
[Abstract]:Electric vehicles have the characteristics of clean and pollution-free, diversified energy sources, high energy efficiency, and easy to realize intelligent management, which can solve a series of problems such as energy, environment and traffic caused by fuel vehicles. Therefore, electric vehicle has become a research hotspot all over the world. Electric vehicle technology in China is still in its infancy, all key technologies need to be studied and solved. Motor and its control technology is one of the key technologies of electric vehicle, which is the first problem to be solved in the research and development of electric vehicle. In this paper, the research object is brushless DC motor, and the control system is designed to make the motor run smoothly and reduce its fluctuation and noise. Firstly, the basic structure of DC brushless motor is analyzed in this paper. According to the mathematical modeling, combined with the application requirement and application range of electric vehicle motor, the simulation model is established in the MATLAB/Simulink simulation platform. The advanced PID algorithm is applied to the established simulation model. Finally, experiments show that the advanced PID algorithm can effectively improve the stability and robustness of the system. On the basis of the completion of the paper. The main work and conclusions are as follows: (1) the application of radial basis function neural network (PID) in brushless DC motor control is simulated by using MATLAB. It is proved that radial basis function neural network (PID) is superior to simple PID in control performance. Obviously, in the aspect of dynamic index, the overshoot is obviously reduced, the frequency of oscillation, the adjustment time and the rising time are shortened. In the aspect of steady-state performance, the change of adjusting time is not obvious. It is proved that the radial basis function neural network PID is superior to the simple PID control performance. (2) Freescale's MC56F801X series chips are used as the core of the main control circuit, and the DRV8301 chip is used in the drive circuit. The hardware foundation of the brushless DC motor control system is composed. In software, the main program and interrupt program of the system are compiled by using the platform CCS,. Finally, the validity of the above method is verified by the combination of software and hardware. (3) the experiment proves that the method is effective. The control strategy of brushless DC motor presented in this paper is correct and feasible. The hardware of the control system is simple, the reliability and robustness of the system are improved, and the control effect is good. The work in this paper provides a certain theoretical reference and practical engineering method for the wide application of brushless DC motor.
【學(xué)位授予單位】：河北科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM33;U469.72

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 鄧元望;曾俊;高洋;鄭潮雄;;BLDCM自調(diào)整比例因子模糊控制系統(tǒng)的建模及仿真[J];微電機;2016年01期

2 劉慧博;王靜;吳彥合;;無刷直流電機模糊自適應(yīng)PID控制研究與仿真[J];控制工程;2014年04期

3 趙一衡;顧偉康;陳亞彬;曹翔;;無刷直流電機轉(zhuǎn)子位置檢測方法綜述[J];機械研究與應(yīng)用;2014年02期

4 王志春;宋吉慶;孫建國;邊長安;;永磁無刷直流電機無位置傳感器控制方法研究[J];工業(yè)控制計算機;2014年03期

5 張洪帥;王平;韓邦成;;基于模糊PI模型參考自適應(yīng)的高速永磁同步電機轉(zhuǎn)子位置檢測[J];中國電機工程學(xué)報;2014年12期

6 辛斌;陳杰;彭志紅;;智能優(yōu)化控制:概述與展望[J];自動化學(xué)報;2013年11期

7 石堅;李鐵才;;一種消除無刷直流電動機換相轉(zhuǎn)矩脈動的PWM調(diào)制策略[J];中國電機工程學(xué)報;2012年24期

8 任晶瑩;姚緒梁;蔡晶;趙云凱;;基于神經(jīng)網(wǎng)絡(luò)控制器的直流無刷電動機控制研究[J];農(nóng)機化研究;2012年06期

9 朱穎合;薛凌云;黃偉;;基于自組織調(diào)整因子的模糊PID控制器設(shè)計[J];系統(tǒng)仿真學(xué)報;2011年12期

10 徐麗麗;何志琴;馬凱;;無刷直流電機新型控制方案的仿真研究[J];工業(yè)控制計算機;2011年05期

相關(guān)碩士學(xué)位論文 前7條

1 徐承愛;無刷直流電機智能控制策略的研究與仿真[D];廣東工業(yè)大學(xué);2015年

2 趙朋成;無刷直流電機的無位置傳感器控制技術(shù)研究[D];華南理工大學(xué);2014年

3 陳錕;無刷直流電機智能直接轉(zhuǎn)矩控制研究[D];昆明理工大學(xué);2013年

4 鄒月海;基于模糊控制的永磁無刷直流電機調(diào)速系統(tǒng)研究[D];哈爾濱工程大學(xué);2009年

5 包曉明;基于DSP的無刷直流電機模糊PI控制系統(tǒng)研究[D];江蘇大學(xué);2007年

6 戴瑩;基于BP神經(jīng)網(wǎng)絡(luò)的無刷直流電機PID控制方法的研究[D];合肥工業(yè)大學(xué);2007年

7 李志強;基于RBF神經(jīng)網(wǎng)絡(luò)在線辨識的永磁無刷直流電機單神經(jīng)元自適應(yīng)PID控制[D];天津大學(xué);2005年

，

本文編號：2271269