【摘要】：為了解決礦用人車平順性較差的問題，結(jié)合車輛平順性試驗(yàn)方法，將改進(jìn)粒子群算法、靈敏度分析方法和近似建模理論應(yīng)用到求解懸架多參數(shù)優(yōu)化問題當(dāng)中，并通過試驗(yàn)驗(yàn)證，提出一種有效的懸架多參數(shù)優(yōu)化方法。 本文按照從低自由度到高自由度建模，從簡單到復(fù)雜分析問題的思路展開。首先基于1/2車輛振動(dòng)模型，以車身振動(dòng)加權(quán)加速度均方根為目標(biāo)函數(shù)，以懸架動(dòng)撓度和輪胎動(dòng)載荷為約束，運(yùn)用標(biāo)準(zhǔn)粒子群算法（SPSO）進(jìn)行車輛四自由度懸架參數(shù)優(yōu)化。針對SPSO算法容易陷入局部最優(yōu)、優(yōu)化速度較慢的問題，通過慣性權(quán)值調(diào)整、超出邊界粒子速度位置選擇、引入混沌變異加強(qiáng)局部搜索和調(diào)整混沌粒子群算法鄰域選取策略等方法提高了粒子群算法尋優(yōu)精度和收斂速度，并提出了指數(shù)函數(shù)調(diào)整慣性權(quán)值和局部鄰域的混沌粒子群算法（ICPSO）。仿真表明：應(yīng)用ICPSO算法可以大大提高懸架優(yōu)化問題的收斂速度和尋優(yōu)精度，獲得最優(yōu)的懸架參數(shù)匹配結(jié)果。 將改進(jìn)粒子群算法應(yīng)用到整車七自由度振動(dòng)模型中，針對粒子群算法多次調(diào)用模型求解運(yùn)算效率低、耗時(shí)長的問題，對影響車輛平順性的18個(gè)懸架主要參數(shù)進(jìn)行靈敏度分析，找出對車輛平順性影響較大的參數(shù)，，并對車輛振動(dòng)模型進(jìn)行響應(yīng)面法近似建模，用二階多項(xiàng)式擬合仿真模型，最后再用ICPSO算法對懸架近似模型進(jìn)行優(yōu)化。仿真表明：應(yīng)用近似模型大大減少了優(yōu)化時(shí)間，提高了優(yōu)化效率，得到了理想的懸架參數(shù)匹配結(jié)果，在懸架動(dòng)撓度變化不大的情況下，大大降低了車身振動(dòng)加權(quán)加速度均方根值和輪胎動(dòng)載荷。 最后參照GB/T4970-2009平順性試驗(yàn)方法對改進(jìn)前和改進(jìn)后的車輛進(jìn)行了隨機(jī)和脈沖路面平順性試驗(yàn)，試驗(yàn)結(jié)果表明：改進(jìn)后的車輛隨機(jī)路面加權(quán)加速度均方根值比改進(jìn)前減小了30%左右，脈沖路面最大加速度響應(yīng)值比改進(jìn)前減小了50%左右，試驗(yàn)結(jié)果證明了基于粒子群算法的懸架多參數(shù)優(yōu)化可以提高車輛的行駛平順性，可以用來指導(dǎo)后續(xù)的懸架開發(fā)和設(shè)計(jì)。
[Abstract]:In order to solve the problem of poor ride comfort of mine vehicles, the improved particle swarm optimization (PSO) algorithm, sensitivity analysis method and approximate modeling theory are applied to solve the multi-parameter optimization problem of suspension. An effective multi-parameter optimization method for suspension is proposed. In this paper, the idea of modeling from low degree of freedom to high degree of freedom and from simple to complex analysis is presented. Firstly, based on 1 / 2 vehicle vibration model, taking the root-mean-square root of vehicle body vibration weighted acceleration as objective function, taking suspension dynamic deflection and tire dynamic load as constraints, the standard particle swarm optimization algorithm (SPSO) is used to optimize vehicle four-degree-of-freedom suspension parameters. Aiming at the problem that SPSO algorithm is easy to fall into local optimum and the speed of optimization is slow, the velocity position of particles beyond the boundary is chosen by adjusting the inertia weight. By introducing chaos mutation to enhance local search and adjust the neighborhood selection strategy of chaotic particle swarm optimization algorithm, the optimization accuracy and convergence speed of particle swarm optimization algorithm are improved, and a chaotic particle swarm optimization algorithm, (ICPSO)., which adjusts inertia weight and local neighborhood by exponential function is proposed. Simulation results show that the convergence speed and precision of suspension optimization problem can be greatly improved by using ICPSO algorithm, and the optimal suspension parameter matching results can be obtained. The improved particle swarm optimization (PSO) algorithm is applied to the vibration model of vehicle with seven degrees of freedom. Aiming at the problems of low efficiency and long time consuming, the main parameters of 18 suspensions which affect the ride comfort of the vehicle are analyzed. The parameters which have great influence on vehicle ride comfort are found, and the response surface method is used to model the vehicle vibration model, the simulation model is fitted with second-order polynomial, and the suspension approximate model is optimized by ICPSO algorithm. The simulation results show that the application of the approximate model can greatly reduce the optimization time, improve the optimization efficiency, and obtain the ideal suspension parameter matching results. When the dynamic deflection of the suspension does not change much, the dynamic deflection of the suspension is not changed. The root-mean-square value of vehicle vibration weighted acceleration and the dynamic load of tire are greatly reduced. Finally, the random and pulse pavement ride comfort tests of vehicles before and after improvement are carried out by referring to the GB/T4970-2009 ride comfort test method. The experimental results show that the RMS value of the improved vehicle random pavement weighted acceleration is reduced by about 30% compared with that before the improvement. The maximum acceleration response of impulse pavement is reduced by about 50% compared with that before improvement. The experimental results show that the multi-parameter optimization of suspension based on particle swarm optimization can improve the ride comfort of vehicles and can be used to guide the subsequent suspension development and design.
【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TD50

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