基于粒子群算法的重型车辆悬架路面友好性非线性优化

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摘要针对重型车辆悬架系统的非线性优化问题，提出了采用粒子群算法进行悬架系统优化设计方法。本文以四分之一汽车模型为研究对象，以车轮最大动载荷最小化为目标，以非簧载质量的固有频率为约束条件，建立了改善被动悬架系统性能的优化模型。通过粒子群算法求解该优化问题，以少量的迭代次数得到了性能优良的悬架参数，如悬架刚度和悬架阻尼。对本文设计的悬架系统进行时域和频域性能分析，并详细讨论了本文方法与遗传算法优化的本质区别，结果表明，粒子群算法可以快速地得到一个较好的优化结果，这为设计人员后续的修改设计提供重要参数，同时为解决含有非线性问题的新型悬架系统优化设计提供了一种新的可行方法。

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	王陆峰

关键词 ：粒子群算法, 重型车辆悬架, 优化设计, 时域分析, 频域分析

Abstract：In this paper, an optimization design method based on the Particle Swarm Optimization (PSO) technique was proposed for the enhancement of the passive suspension system performance of heavy vehicles. The optimization problem based on a quartervehicle model aims to minimize the dynamic tire load subject to constraints on the natural frequency of the unsprung mass. PSO was applied to solve the optimization problem for obtaining optimum parameters, such as the suspension damping coefficient and spring stiffness. In order to assess the obtained design parameters, the suspension performance was estimated in time and frequency domains for different road excitations. And some essential differences between PSO and Genetic Algorithm (GA) were discussed in detail. The results show that, for the same suspension system, the proposed method can obtain the optimum designed parameters quickly compared with the previous designed parameters using the GA, which will provide important parameters for the subsequent revised design.

Key words： particle swarm algorithm heavy vehicle suspension optimization design time domain analysis frequency domain analysis

收稿日期: 2016-09-21 出版日期: 2018-04-15

引用本文:

王陆峰. 基于粒子群算法的重型车辆悬架路面友好性非线性优化[J]. 振动与冲击, 2018, 37(8): 218-224.
WANG Lufeng. Road friendliness optimization of heavy vehicle suspension based on particle swarm algorithm. JOURNAL OF VIBRATION AND SHOCK, 2018, 37(8): 218-224.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2018/V37/I8/218

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