基于PDJI-MOPSO算法的多连杆悬架硬点优化

中国机械工程 ›› 2013, Vol. 24 ›› Issue (13): 1841-1845.

基于PDJI-MOPSO算法的多连杆悬架硬点优化

冯金芝;陈兴;郑松林;侯盛昱

上海理工大学，上海，200093

出版日期:2013-07-10 发布日期:2013-07-11
基金资助:
国家高技术研究发展计划(863计划)资助项目（2012AA110701）；国家自然科学基金资助项目（50875173）；上海市科委科研计划项目资助（11140502000）；上海市科委基础研究重点项目（10JC1411600）；上海汽车工业科技发展基金资助项目（1104）
National High-tech R&D Program of China (863 Program) （No. 2012AA110701）;

National Natural Science Foundation of China（No. 50875173）

Optimization for Multi-link Suspension Hard-points Based on PDJI-MOPSO Algorithm

Feng Jinzhi;Chen Xing;Zheng Songlin;Hou Shengyu

University of Shanghai for Science and Technology,Shanghai,200093

Online:2013-07-10 Published:2013-07-11
Supported by:

National High-tech R&D Program of China (863 Program) （No. 2012AA110701）;

National Natural Science Foundation of China（No. 50875173）

摘要/Abstract

摘要：

基于刚体运动学理论建立了多连杆悬架的数学模型，并通过运用多体系统动力学理论建立的仿真模型对其进行了验证。采用扰动法对影响悬架K&C特性较大的关键硬点进行了多因素灵敏度分析。结合加入比例分布及跳数操作的多目标粒子群优化(PDJI-MOPSO)算法对硬点进行多目标优化，获得硬点的Pareto最优解集。结果表明，优化后的多连杆悬架在车轮跳动过程中，车轮前束角、外倾角和轮距变化范围更小，有利于减小轮胎磨损，提高汽车操纵稳定性。

关键词: 多连杆悬架, 硬点, 多因素灵敏度分析, PDJI-MOPSO算法

Abstract:

Based on the rigid body kinematics theory, a mathematical model of a multi-link suspension was established, which was followed by the validation using the corresponding simulation model based on multi-body system dynamics theory. With perturbation method, multiple factors sensitivity analysis was researched about key hard-points of affecting suspension K&C characteristics, which were optimized by applying the PDJI-MOPSO algorithm, and the Pareto optimal solution set was obtained. The results show that the values of camber angle, toe angle and wheel travel track are reduced. The optimized suspension is more conducive to vehicle handling stability, and reduces tire wearing loss as well.

Key words: multi-link suspension, hard-point, multiple factors sensitivity analysis, PDJI-MOPSO algorithm

中图分类号:

U463

冯金芝, 陈兴, 郑松林, 侯盛昱. 基于PDJI-MOPSO算法的多连杆悬架硬点优化[J]. 中国机械工程, 2013, 24(13): 1841-1845.

FENG Jin-Zhi, CHEN Xin, ZHENG Song-Lin, HOU Cheng-Yu. Optimization for Multi-link Suspension Hard-points Based on PDJI-MOPSO Algorithm[J]. China Mechanical Engineering, 2013, 24(13): 1841-1845.

参考文献

[1]Huang K, Yu F ，Zhang Y. Active Controller Design for an Electromagnetic Energy-regenerative Suspension[J]. International Journal of Automotive Technology,2011,12（6）: 877-885.
[2]卢少波，李以农，郑玲.基于制动与悬架系统的车辆主动侧翻控制的研究[J].汽车工程,2011,33（8）：669-675.
Lu Shaobo,Li Yinong,Zheng Ling. The Research for Automotive Active Side Control Based on the Braking and Suspension System[J]. Automotive Engineering,2011,33（8）：669-675.
[3]吴旭东，左曙光，雷镭，等.基于摩擦自振的轮胎-悬架振动系统仿真分析[J].振动与冲击,2011,30（9）：89-93.
Wu Xudong,Zuo Shuguang,Lei Lei,et al. Simulation Analysis Based on the Vibration System of Friction Vibration between the Type and Suspension[J].Journal of Vibration and Shock, 2011,30（9）：89-93.
[4]Jeff W, Norman F. A Facility for the Measurement of Heavy Truck Chassis and Suspension Kinematics and Compliances[C]//SAE Commercial Vehicle Engineering Congress Exhibition. New York,2004: 113-119.
[5]Caputo A, Spina M, Guglielmino E. Sensitivity of Suspension System Performance to Bushing Stiffness Variation: An Evaluation Methodology[C]//SAE Commercial Vehicle Engineering Congress Exhibition. New York, 2003:166-177.
[6]Simionescu P A, Beale D. Synthesis and Analysis of the Five-link Rear Suspension System Used in Automobiles[J]. Mechanism and Machine Theory, 2002,37:815-832.
[7]宋健,石磊.多连杆式前悬架主销轴线的确定[J].公路交通科技,2001,18(6): 99-102.
Song Jian, Shi Lei. Determination of Multi-link Front Suspension Kingpin Axis[J].Journal of Highway and Transportation, 2001,18(6):99-102.
[8]奉铜明.汽车多连杆悬架的多目标优化与分析[D].长沙：湖南大学，2011.
[9]逄淑一.某汽车悬架运动学及弹性运动学灵敏度分析及改进[D].长春：吉林大学，2007.
[10]刘伟忠. 基于虚拟样机技术的某车悬架K&C特性仿真分析及硬点优化[D]. 长春：吉林大学, 2009.
[11]Zitzler E, Laumanns M, Thiele L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multi-objective Optimization[C]//Evolutionary Methods for Design and Control with Applications to Industrial Problems. Athens: International Center for Numerical Methods in Engineering, 2001: 95-100.
[12]Deb K, Pratap A, Agarwal S, et al. A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II[J].IEEE Transactions on Evolutionary Computation, 2002(6): 182-197.
[13]Carlos A. A Proposal for Multiple Objective Particle Swarm Optimization[C]// Evolutionary Computation, 2002. CEC’02. Piscataway, New Jersey, 2002：1051-1056.
[14]安伟刚.多目标优化方法研究及其工程应用[D].西安：西北工业大学，2005.
[15]Tsai Shang-Jeng, Sun Tsung-ying, Liu Chan-Cheng, et al. An Improved Multi-objective Particle Swarm Optimizer for Multi-objective Problems[J]. Expert Systems with Applications, 2010(37):5872-5886.