Optimizing Vehicle Ride Comfort Based on Uncertainty Theory and Fuzzy Theory
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摘要: 以整车八自由度振动模型为对象,建立某车行驶平顺性的MATLAB/SMULINK模型,根据仿真结果构建了Kriging模型。以座椅加权加速度均方根值为目标函数,以悬架的刚度和非线性阻尼系数、座椅的刚度和阻尼为设计变量,以簧上质量、轮胎的刚度和阻尼为不确定模糊变量,运用模糊理论和多种群遗传算法对该仿真模型进行双层嵌套的不确定性优化。对比表明:不确定性量在一定范围变化时,确定性优化的目标函数恶化到2.0 m/s2,远远大于不确定性优化时目标函数的最小值(1.5 m/s2),即不确定性优化的结果对行驶过程中的不确定性量变化更加不敏感,适应性更好。Abstract: In order to enhance the adaptability of the ride comfort optimization result under uncertainties when a vehicle is moving, the paper studies the 8-DOF vibration model of the whole vehicle and then sets up the MATLAB/SIMULINK model of its ride comfort. To improve the optimization calculation efficiency, the paper constructs theKriging model according to the simulation results. The mean square value of acceleration is taken as the objectivefunction; the suspension stiffness and nonlinear damping coefficients, the seat's stiffness and damping are taken asdesign variables; the quality of spring and the stiffness and damping of tires are taken as uncertainties and fuzziness. Using the fuzzy theory and the multi-population genetic algorithm, the double-nested uncertainty of the modelis optimized. The comparison indicates that when the range of uncertainty is varying, the optimization results reach2. 0 m/s2, far greater than the optimization results under uncertainty (1. 5 m/s2); that is to say, the optimizationresults under uncertainty have better adaptability.
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Key words:
- vehicle ride comfort /
- adaptability /
- optimization /
- uncertainty
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