本文關(guān)鍵詞：發(fā)動(dòng)機(jī)半主動(dòng)懸置模糊控制策略及AMESim仿真研究　出處：《長(zhǎng)安大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 發(fā)動(dòng)機(jī)懸置 半主動(dòng)懸置 模糊控制 粒子群優(yōu)化算法 AMESim/Simulink聯(lián)合仿真

【摘要】：發(fā)動(dòng)機(jī)是汽車的動(dòng)力源,也是汽車主要的噪聲源與振動(dòng)源,對(duì)汽車的乘坐舒適性有較大的影響。懸置是發(fā)動(dòng)機(jī)與車架(車身)之間用以對(duì)發(fā)動(dòng)機(jī)進(jìn)行支承,并減少振動(dòng)向車架(車身)傳遞的裝置,它由剛度元件和阻尼元件組成。傳統(tǒng)的被動(dòng)懸置其剛度和阻尼值保持不變,無(wú)法滿足發(fā)動(dòng)機(jī)多工況的隔振要求,而主動(dòng)懸置耗能大、成本高、結(jié)構(gòu)復(fù)雜。因此,性能優(yōu)良的半主動(dòng)懸置受到了眾多研究者的重視。半主動(dòng)懸置由剛度元件和可調(diào)阻尼元件組成,它通過調(diào)節(jié)阻尼值,滿足發(fā)動(dòng)機(jī)在不同工況的隔振要求。本論文以發(fā)動(dòng)機(jī)半主動(dòng)懸置為對(duì)象,研究基于模糊控制的半主動(dòng)懸置控制策略,并采用粒子群優(yōu)化算法對(duì)模糊控制器參數(shù)進(jìn)行優(yōu)化。本論文研究?jī)?nèi)容包括:(1)分析了單缸發(fā)動(dòng)機(jī)活塞—連桿—曲軸機(jī)構(gòu)在運(yùn)動(dòng)過程中的受力情況,研究了單缸發(fā)動(dòng)機(jī)工作時(shí)的主要激勵(lì)力和力矩。在此基礎(chǔ)上,推導(dǎo)了四缸直列發(fā)動(dòng)機(jī)振動(dòng)的主要激勵(lì)力和力矩,得出四缸發(fā)動(dòng)機(jī)激勵(lì)力主要為二階不平衡往復(fù)慣性力的結(jié)論。(2)建立了基于被動(dòng)懸置和半主動(dòng)懸置的發(fā)動(dòng)機(jī)動(dòng)力學(xué)模型,給出了系統(tǒng)運(yùn)動(dòng)微分方程。介紹了多學(xué)科復(fù)雜系統(tǒng)建模與仿真軟件AMESim,說明了該軟件主要模塊的功能。利用AMESim軟件建立了基于半主動(dòng)懸置的發(fā)動(dòng)機(jī)模型。(3)研究了模糊控基礎(chǔ)理論,設(shè)計(jì)了發(fā)動(dòng)機(jī)半主動(dòng)懸置模糊控制器。利用AMESim和Simulink對(duì)采用模糊控制的發(fā)動(dòng)機(jī)半主動(dòng)懸置的隔振性能進(jìn)行了聯(lián)合仿真分析,結(jié)果表明模糊控制半主動(dòng)懸置能夠有效地減小發(fā)動(dòng)機(jī)振動(dòng)向車架的傳遞率。(4)研究了粒子群優(yōu)化算法,采用該算法優(yōu)化了模糊控制器量化因子和比例因子。對(duì)采用優(yōu)化的模糊控制器和未優(yōu)化模糊控制器的發(fā)動(dòng)機(jī)半主動(dòng)懸置隔振性能進(jìn)行了仿真研究,結(jié)果表明粒子群算法可以改善模糊控制器的性能,提高半主動(dòng)懸置的隔振性能。
[Abstract]:The engine is the power source of the automobile, the car is the main noise source and vibration source, has great influence on riding comfort. Mount is the engine and the frame (body) between the support on the engine, and reduce vibration to the frame (body) transmission device, which is composed of stiffness components and damping elements. The traditional passive suspension stiffness and damping values remain unchanged, unable to meet the vibration condition of the engine, and the active suspension of high energy consumption, high cost and complicated structure. Therefore, the excellent performance of the semi-active suspension is paid more attention by many researchers. The semi active suspension by stiffness element and the adjustable damping components, which by adjusting the damping value of engine vibration isolation in meet the requirements of different working conditions. The engine semi-active suspension as the object, the research of fuzzy control of semi active suspension control strategy based on the particle swarm, and Optimization of fuzzy controller parameters optimization algorithm. The content of this paper includes: (1) analysis of single cylinder engine piston rod crank mechanism during exercise stress, the main excitation force and torque of single cylinder engine at work. On this basis, we derive four cylinder in-line engine vibration is mainly excitation force and torque, the four cylinder engine excitation force is mainly two order unbalanced reciprocating inertia force conclusion. (2) established the engine dynamic model of passive suspension and semi-active suspension system based on the differential equations of motion are given. The complex system modeling and simulation software AMESim, illustrates the main the module function of the software. The model of the engine semi-active suspension based on AMESim software. (3) studied the fuzzy control theory, the design of the engine semi-active suspension fuzzy controller. The vibration isolation performance of fuzzy control is adopted for AMESim and Simulink engine semi-active suspension were combined with simulation analysis, the results show that the fuzzy control of semi active suspension can effectively reduce the vibration of the engine to the frame transfer rate. (4) research of particle swarm optimization algorithm, using the algorithm to optimize the fuzzy controller quantization factor and the scaling factor. The optimized fuzzy controller and fuzzy controller optimization engine semi-active suspension performance was simulated. The results show that the performance of particle swarm algorithm can improve the fuzzy controller, improve the isolation performance of the semi-active suspension.

【學(xué)位授予單位】：長(zhǎng)安大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：U464.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李銳;陳偉民;廖昌榮;張紅輝;李銀國(guó);;基于磁流變技術(shù)的發(fā)動(dòng)機(jī)隔振控制[J];機(jī)械工程學(xué)報(bào);2009年03期

2 陳，

本文編號(hào)：1384038