应用多目标粒子群的驾驶室悬置参数联合仿真优化

姚昱儒; 毕凤荣; 景亚兵; 齐彬; 田赛龙; 田从丰

doi:10.13433/j.cnki.1003-8728.2017.0419

应用多目标粒子群的驾驶室悬置参数联合仿真优化

doi: 10.13433/j.cnki.1003-8728.2017.0419

1.
天津大学内燃机燃烧学国家重点实验室, 天津 300072;
2.
天津大学机械工程学院, 天津 300072;
3.
山推工程机械股份有限公司, 山东济宁 272000

基金项目:

国家科技支撑计划项目（2015BAF07B04）资助

详细信息

作者简介:
姚昱儒(1992-),硕士研究生,研究方向为工程机械减振关键技术,evelyn_yao@163.com

通讯作者:
景亚兵(联系人),高级工程师,博士,robin@tju.edu.cn

计量
- 文章访问数: 213
- HTML全文浏览量: 34
- PDF下载量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2015-06-14
- 刊出日期: 2017-04-05

Co-simulation of Cab Mounting System Optimization using MOPSO Algorithm

1.
State Key Laboratory of Engines, Tianjin University, Tianjin 300072, China;
2.
School of Mechanical Engineering, Tianjin University, Tianjin 300072, China;
3.
Shantui Construction Machinery Co., Ltd., Shangdong Jining 272000, China

摘要

摘要: 为简化悬置系统开发流程、缩短周期，以某型装载机驾驶室悬置系统为对象，在ADAMS中建立虚拟样机模型，采用试验测试信号作为激励，并在MATLAB中编写了优化算法程序，通过ADAMS和MATLAB联合仿真的方法，解决了驾驶室悬置隔振性能的多目标优化问题。以振动总量值最小化和解耦率最大化作为优化目标，分别采用了多目标粒子群算法（MOPSO）与非支配排序遗传算（NSGA-Ⅱ）法，结果表明前者能找到更好的帕累托前沿，仿真分析验证了该方法的可行性和有效性。
- 驾驶室悬置 /
- 联合仿真 /
- 隔振 /
- MOPSO /
- 能量解耦
Abstract: In order to simplify development process and cut down the period of cab mounting system, the co-simulation method is used to solve the multi-objective optimization problem of a loader cab mounting system's vibration isolation performance whose virtual prototype is built in ADAMS, excitation signals come from experimental test and optimization algorithms are coded in MATLAB. The objective includes minimization of vibration total value and maximization decoupling ratio. The multi-objective particle swarm optimization (MOPSO) algorithm shows a better optimization performance than non-dominated sorting genetic (NSGA-Ⅱ) algorithm by covering a better Pareto frontier in this problem. Simulation results also confirm the feasibility and effectiveness of the approach in this study.
- cab mounting system /
- co-simulation /
- MATLAB /
- vibration isolation /
- particle swarm optimization (PSO)

HTML全文

参考文献(14)

[1]	Zehsaz M, Sadeghi M H, Ettefagh M M, et al. Tractor cabin's passive suspension parameters optimization via experimental and numerical methods[J]. Journal of Terramechanics, 2011,48(6):439-450
[2]	Alfi A, Fateh M M. Identification of nonlinear systems using modified particle swarm optimisation: a hydraulic suspension system[J]. Vehicle System Dynamics, 2011,49(6):871-887
[3]	Wang W, Li G X, Song Y L. Nonlinear dynamic analysis of the whole vehicle on bumpy road[J]. Transactions of Tianjin University, 2010,16(1):50-55
[4]	Mahesh P, Wali S, Kale S. Dynamic analysis of cabin tilting system of heavy trucks using ADAMS-View for development of a software interface for optimization[R]. SAE Technical Paper 2008-01-2683, 2008
[5]	王楷焱,史文库,杨昌海,等.基于ADAMS的商用车驾驶室悬置系统的振动模态和传递特性[J].吉林大学学报(工学版),2010,40(2):330-334 Wang K Y, Shi W K, Yang C H, et al. Commercial vehicle cab suspension system vibration modes and transmission characteristics by means of ADAMS[J]. Journal of Jilin University (Engineering and Technology Edition), 2010,40(2):330-334 (in Chinese)
[6]	Scheiblegger C, Roy N, Parez O S, et al. Non-linear modeling of bushings and cab mounts for calculation of durability loads[R]. SAE Technical Paper 2014-01-0880, 2014
[7]	黄晨,陈龙,袁朝春,等.半主动悬架系统的混合模糊控制[J].汽车工程,2014,36(8):999-1003,1018 Huang C, Chen L, Yuan Z C, et al. Hybrid fuzzy control of semi-active suspension system[J]. Automotive Engineering, 2014,36(8):999-1003,1018 (in Chinese)
[8]	中华人民共和国国家质量监督检验检疫总局中国国家标准化管理委员会.GB/T 13441.1-2007 机械振动与冲击人体暴露于全身振动的评价第一部分:一般要求[S].北京:中国标准出版社,2007 The State Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, China National Standardization Management Committee. GB/T 13441.1-2007 Mechanical vibration and shock-evaluation of human exposure to whole-body vibration: part 1: general requirements[S]. Beijing: China Standards Press, 2007 (in Chinese)
[9]	El Hafidi A, Martin B, Loredo A, et al. Vibration reduction on city buses: Determination of optimal position of engine mounts[J]. Mechanical Systems and Signal Processing, 2010,24(7):2198-2209
[10]	Pasandideh S H R, Niaki S T A, Sharafzadeh S. Optimizing a bi-objective multi-product EPQ model with defective items, rework and limited orders: NSGA-II and MOPSO algorithms[J]. Journal of Manufacturing Systems, 2013,32(4):764-770
[11]	Eberhart R C, Kennedy J. A new optimizer using particle swarm theory[C]//Proceedings of the sixth international symposium on micro machine and human science, Nagoya: IEEE, 1995,1:39-43
[12]	Coello C A C, Pulido G T, Lechuga M S. Handling multiple objectives with particle swarm optimization[J]. IEEE Transactions on Evolutionary Computation,2004,8(3):256-279
[13]	Khalili M, Tavakkoli-Moghaddam R. A multi-objective electromagnetism algorithm for a bi-objective flowshop scheduling problem[J]. Journal of Manufacturing Systems, 2012,31(2):232-239
[14]	Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002,6(2):182-197