混合碰撞建模方法及其试验验证

doi:10.3901/JME.2022.01.116

机械工程学报 ›› 2022, Vol. 58 ›› Issue (1): 116-123.doi: 10.3901/JME.2022.01.116

扫码分享

混合碰撞建模方法及其试验验证

刘茜^1,2, 程靖³, 梁建勋³

1. 北京航天飞行控制中心北京 100094;
2. 航天飞行动力学技术重点实验室北京 100094;
3. 清华大学航天航空学院北京 100084

收稿日期:2021-02-05 修回日期:2021-08-05 出版日期:2022-01-05 发布日期:2022-03-19
作者简介:刘茜,女,1989年出生,博士后。主要研究方向为空间机械臂动力学与控制、多刚体系统动力学、接触碰撞动力学。E-mail:liuqian_bit@163.com

Novel Hybrid Method for Contact Analysis and Experiment Test

LIU Qian^1,2, CHENG Jing³, LIANG Jianxun³

1. Beijing Aerospace Flight Control Center, Beijing 100094;
2. Key Laboratory of Science and Technology on Aerospace Flight Dynamics, Beijing 100094;
3. School of Aerospace Engineering, Tsinghua University, Beijing 100084

Received:2021-02-05 Revised:2021-08-05 Online:2022-01-05 Published:2022-03-19

摘要/Abstract

摘要： 目前广泛采用的接触碰撞建模方法主要基于传统的物理碰撞模型，如赫兹碰撞模型和非线性弹簧阻尼碰撞模型等，但它们的仿真精度不够高，更适用于接触碰撞趋势的仿真分析。基于此，提出了基于传统物理碰撞模型和数据驱动误差模型的混合碰撞建模方法，以更加准确地对接触碰撞现象进行动力学仿真分析。其中，物理碰撞模型对接触碰撞现象中已知的碰撞因素进行建模；基于径向基函数（Radial basis function，RBF）神经网络的数据驱动误差模型对接触碰撞现象中的误差部分进行建模。小球自由落体碰撞试验对混合碰撞建模方法进行试验验证，试验结果验证了混合碰撞建模方法的可行性和混合碰撞模型的准确性。

关键词: 接触碰撞动力学, 接触碰撞建模方法, 数据驱动建模, 径向基函数神经网络, 基于遗传算法的改进粒子群(Particalswarmoptimization, PSO)优化算法

Abstract: The widely used contact modeling methods are mainly based on traditional physical contact models, such as Hertz model and nonlinear contact models. However, their simulation results are not accurate enough, which makes them more suitable for the trend analysis of contact phenomena. In this paper, a hybrid contact modeling method, based on the traditional physical model and the data-driven error model, is proposed to accurately simulate the contact phenomenon. The physical contact model simulates the known contact factors in the contact phenomenon, and the data-driven error model based on the radial basis function neural network model describes the errors between the contact phenomenon and the simulation result of the physical contact model. A bouncing ball contact test is proposed to verify the hybrid contact modeling method, and the test result shows the feasibility of the hybrid contact modeling method and the accuracy of the hybrid contact model.

Key words: contact dynamics, contact modeling method, data-driven model, radial basis function neural network model, improved PSO algorithm based on genetic operator

中图分类号:

O313

刘茜, 程靖, 梁建勋. 混合碰撞建模方法及其试验验证[J]. 机械工程学报, 2022, 58(1): 116-123.

LIU Qian, CHENG Jing, LIANG Jianxun. Novel Hybrid Method for Contact Analysis and Experiment Test[J]. Journal of Mechanical Engineering, 2022, 58(1): 116-123.

参考文献

[1] SHEN Yinhua,XIANG Dong,WANG Xiang,et al. A contact force model considering constant external forces for impact analysis in multibody dynamics[J]. Multibody System Dynamics,2018,44(4):397-419.
[2] 钟阳,钟志华,李光耀,等. 机械系统接触碰撞界面显式计算的算法综述[J]. 机械工程学报,2011,47(13):44-58. ZHONG Yang,ZHONG Zhihua,LI Guangyao,et al. Review on contact algorithms calculating the contact-impact interface in mechanical system with explicit FEM[J]. Journal of Mechanical Engineering,2011,47(13):44-58.
[3] PAULO F,MARGARIDA M,MIGUEL T,et al. On the continuous contact force models for soft materials in multibody dynamics[J]. Multibody System Dynamics,2011,25(3):357-375.
[4] KENNETH H,ERSKINE C. Coefficient of restitution interpreted as damping in vibroimpact[J]. Journal of Applied Mechanics,1975,42(2):440-445.
[5] YU Lei,ZHAO Zhihua,REN Gexue. Multibody dynamic model of web guiding system with moving web[J]. Journal of Dynamic Systems Measurement and Control,2010,132(5):051004.
[6] 蔡显新,齐思鑫,吴春来,等. 航空发动机静强度设计中的保护准则探讨[J]. 机械工程学报,2017,53(13):101-107. CAI Xianxin,QI Sixin,WU Chunlai,et al. Investigation on the protect criteria of aero-engine static strength design[J]. Journal of Mechanical Engineering,2017,53(13):101-107.
[7] LUKA S,JANKO S,MIHA B. A review of continuous contact-force models in multibody dynamics[J]. International Journal of Mechanical Sciences,2018,145:171-187.
[8] WENBER M,PATEL K,MA O,et al. Identification of contact dynamics model parameters from constrained robotic operations[J]. Journal of Dynamic Systems Measurement and Control,2006,128(2):307-318.
[9] 高玉峰,李晓斌,杨永清. 桥梁结构地震碰撞问题理论分析模型及试验研究进展[J]. 应用力学学报,2010,27(4):809-816. GAO Yufeng,LI Xiaobin,YANG Yongqing. Advances in analytical models and experimental studies on earthquake-induced pounding between adjacent bridge decks[J]. Chinese Journal of Applied Mechanics,2010,27(4):809-816.
[10] CAO Dengqing,YANG Yang,CHEN Huatao,et al. A novel contact force model for the impact analysis of structures with coating and its experimental verification[J]. Mechanical Systems and Signal Processing,2016,70-71:1056-1072.
[11] 王海涛,刘伟强,杨建国. 基于RBF神经网络的磨削表面粗糙度预测模型[J]. 应用力学学报,2014,42(3):107-111. WANG Haitao,LIU Weiqiang,YANG Jianguo. Grinding surface roughness prediction model based on RBF neural network[J]. Journal of Sound Vibration,2014,42(3):107-111.
[12] CARNEIRO J,ALMEIDA F. A neural network based nonlinear model of a servopneumatic system[J]. Journal of Dynamic Systems Measurement and Control,2012,134(2):024502.
[13] TOBIAS W,BENJAMIN W,PATRICK S,et al. Data-driven learning for calibrating galvanometric laser scanners[J]. IEEE Sensors Journal,2015,15(10):5709-5717.
[14] SALAM M,AL-ALAWI S,MAQRASHI A. An artificial neural networks approach to model and predict the relationship between the grounding resistance and length of buried electrode in the soil[J]. Journal of Electrostatics,2006,64(5):338-342.
[15] SHISHIR K,ELDON R,HUNG-SUCK P. Neural network modeling of sorption of pharmaceuticals in engineered floodplain filtration system[J]. Expert Systems with Applications,2012,39(5):6052-6060.
[16] GILARDI G,SHARF I. Literature survey of contact dynamics modelling[J]. Mechanism and Machine Theory,2002,37(10):1213-1239.
[17] HAMID M,PARVIZE E. Continuous contact force models for impact analysis in multibody systems[J]. Nonlinear Dynamics,1994,5(2):193-207.
[18] JI Shunying,SHEN H. Effect of contact force models on granular flow dynamics[J]. Journal of Engineering Mechanics,2006,132(11):1252-1259.
[19] HU Yusha,LI Jigeng,HONG Mengna,et al. Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm-A case study of papermaking process[J]. Energy,2019,170:1215-1227.
[20] 卢克东,徐良骥,牛亚超. 基于GA-PSO融合算法的开采沉陷Richards预计模型参数优化[J]. 金属矿山,2021,50(2):155-160 LU Kedong,XU Liangji,NIU Yachao. Parameter optimization on richards model of mining subsidence based on GA-PSO hybrid algorithm[J]. Metal Mine,2021,50(2):155-160.
[21] 党选举,谭永红. 压电陶瓷回环特性的RBF神经网络建模研究[J]. 系统仿真学报,2005,17(1):144-147. DANG Xuanju,TAN Yonghong. Study on the RBF neural network hysteresis modeling for piezoceramic actuator[J]. Journal of System Simulation,2005,17(1):144-147.

混合碰撞建模方法及其试验验证

Novel Hybrid Method for Contact Analysis and Experiment Test

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics

本文评价

[1]	张德权, 李星奥, 张宁, 宁国松, 韩旭. 工业机器人全域误差场精细化建模方法及其误差补偿策略[J]. 机械工程学报, 2024, 60(13): 316-329.
[2]	刘金林, 曾凡明, 杨立, 吴杰长, 李文剑, 王泰翔. 舰船轴系设计质量控制模型构建方法及应用研究[J]. 机械工程学报, 2018, 54(10): 211-217.
[3]	吴玉香;张景;王聪. 机械臂的自适应神经网络控制与学习[J]. , 2013, 49(15): 42-48.
[4]	孙贤安;吴光强. 双离合器式自动变速器车辆换挡品质评价系统[J]. , 2011, 47(8): 146-151.
[5]	陈志煌;陈力. 闭链双臂空间机器人抓持载荷基于径向基函数神经网络的补偿控制[J]. , 2011, 47(7): 38-44.
[6]	彭晓燕;章兢;陈昌荣. 基于RBF神经网络的最佳滑移率在线计算方法[J]. , 2011, 47(14): 108-113.
[7]	贾志新;张宏斌;郗安民. 基于径向基函数神经网络的电火花线切割机床可靠性数据模拟生成[J]. , 2010, 46(2): 145-149.
[8]	高焕明;王爱民;陈华伟;蔡智渊. 面向机加与装配混合生产系统的建模与仿真技术[J]. , 2010, 46(19): 155-164.
[9]	魏立新;李兴强;李莹;杨景明. 基于改进自适应遗传算法的冷连轧轧制规程优化设计[J]. , 2010, 46(16): 136-141.
[10]	高贯斌;王文;林铿;陈子辰. 基于RBF神经网络的关节转角误差补偿[J]. , 2010, 46(12): 20-24.
[11]	张鹏贤;张宏杰;陈剑虹;马跃洲. 基于电极位移信号特征分析的电阻点焊质量监测[J]. , 2006, 42(10): 176-181.
[12]	马跃洲;陈剑虹;梁卫东. GMAW电弧声的参数化模型及应用[J]. , 2005, 41(11): 109-114.