针对前悬架确定性优化没有考虑参数不确定性时存在的问题,提出了一种考虑不确定性因素的前悬架不确定性优化方法.应用ADAMS/Car建立了某轿车麦弗逊式前悬架模型,在ADAMS/Insight中进行了悬架设计硬点参数的灵敏度分析,针对灵敏度较大的设计硬点参数和所考虑的不确定性量,建立了车轮定位参数在车轮跳动过程中的Kriging近似模型,简化了优化过程.针对该近似模型进行了动力学分析,运用经过改进的非支配排序遗传算法(NSGA-Ⅱ)和隔代遗传算法(IP-GA)进行了多目标不确定性优化求解.优化结果表明,该不确定性优化方法具有较高的有效性和精确性.与确定性优化结果的对比表明,不确定性优化具有更好的鲁棒性.%For the optimization of a front suspension was almost based on certainty optimization without considering all kinds of uncertainty in the process of practical work,a uncertainty optimization method of the front suspension was proposed. Macpherson suspension model of a car was built based on ADAMS/Car. The design parameter's sensitivity was analyzed by ADAMS/insight. And then, a Kriging approximate model was established for the greater sensitivity of design parameters and uncertain parameters in the wheel beating which simplified the optimization process. The model's stability was analyzed,the NSGA- Ⅱ and the IP-GA were used to solve the multi -objective uncertainty optimization problem. The optimization results indicate that these methods have high accuracy and validity. Finally,the results were compared with those of certainty optimization,which proves that uncertainty optimization has better robustness.
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