阻尼器
磁流变液
粒子群优化
磁流变阻尼器
汽车工业
参数统计
先验与后验
工程类
航程(航空)
参数化模型
控制理论(社会学)
系统标识
实验数据
非线性系统
计算机科学
控制工程
人工智能
算法
数据建模
数学
物理
控制(管理)
量子力学
统计
航空航天工程
哲学
软件工程
认识论
作者
Stratis Kanarachos,Dzmitry Savitski,Nikos D. Lagaros,Michael E. Fitzpatrick
出处
期刊:Soft Computing
[Springer Nature]
日期:2017-08-03
卷期号:22 (24): 8131-8149
被引量:11
标识
DOI:10.1007/s00500-017-2757-6
摘要
The present study discusses the mechanical behaviour and modelling of a prototype automotive magnetorheological (MR) damper, which presents different viscous damping coefficients in jounce and rebound. The force generated by the MR damper is measured at different velocities and electrical currents, and a modified damper model is proposed to improve fitting of the experimental data. The model is calibrated by means of parameter identification, and for this purpose a new swarm intelligence algorithm is proposed, that we call the contrast-based Fruit Fly Optimisation Algorithm (c-FOA). The performance of c-FOA is compared with that of Genetic Algorithms, Particle Swarm Optimisation, Differential Evolution and Artificial Bee Colony. The comparison is made on the basis of no a-priori knowledge of the damper model parameters range. The results confirm the good performance of c-FOA under parametric range uncertainty. A sensitivity analysis discusses c-FOA’s performance with respect to its tuning parameters. Finally, a ride comfort simulation study quantifies the discrepancies in the results, for different identified damper model sets. The discrepancies underline the importance of accurately describing MR damper nonlinear behaviour, considering that virtual sign-off processes are increasingly gaining momentum in the automotive industry.
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