晕动病中车辆横向加速度与头部倾斜角关系的传递函数模型比较

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yassir Ali, Sarah 'Atifah Saruchi
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引用次数: 0

摘要

晕动病(MS)被描述为由剧烈运动引起的不愉快的感觉;在过弯道时,驾驶员和乘客的头部倾斜程度不同,从而影响了他们的晕动病发生率(MSI),即MS的严重程度。MS是一种影响乘员舒适度的负面感觉,为了进一步了解MS中乘员行为与车辆运动的相关性,并将其用数学模型表示,证明了MSI可以通过数学模型来预测。然而,乘员行为与车辆运动之间的值是不确定的。在此基础上,如何用数学的方法来表示其相关性就显得尤为重要。采用前人研究的实验方法,利用系统识别(system identification, SI)方法获取数据,建立不同比例的数学模型,在传递函数方程中表示车辆运动与晕车乘员行为之间的相关性,利用黑盒特征将实验数据作为输入和输出,使SI能够预测传递函数模型。本研究的目的是探讨与车辆运动和乘员行为相关的MS因素,建立多个传递函数模型,并对其进行分析和比较。在驾驶员和乘客各比例使用的二阶、三阶和四阶传递函数阶下得到了模型的拟合结果,驾驶员模型的拟合结果在64.68% ~ 67.87%之间,乘客模型的拟合结果在63.75% ~ 67.93%之间,通过比较得到了各阶的最高拟合结果。驾驶员模型的拟合度最高,分别为67.87%(4阶)、66.78%(3阶)和65.17%(2阶);乘用车模型的拟合度最高,分别为67.93%(4阶)、66.3%(3阶)和64.82%(2阶)。然后通过Simulink使用未在识别过程中使用的未见数据验证这些拟合,最后获得所有模型的均方根误差(RMSE),以确定其效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of Transfer Function Models to Represent the Correlation Between Vehicle Lateral Acceleration and Head Tilting Angle in Motion Sickness
Motion Sickness (MS) is described as an unpleasant feeling caused by a forceful movement; hence vehicle movement impacts the severity of MS. While negotiating a curve, drivers and passenger tilt their heads differently, affecting their motion sickness incidence (MSI), which is the severity of MS. MS is a negative feeling, that affects occupant’s comfort, and to further understand the correlation between occupants' behavior and vehicle movement in MS and then represent it using mathematical models, it was proven that MSI could be predicted through mathematical models. However, there is an indefinite value between values between occupant’s behavior and vehicle movement. Based on that it is vital to express it the correlation mathematically. An experiment adopted from a prior study was utilized to get the data and develope the mathematical models with different proportions to represent the correlation between vehicle movement and occupant behavior in motion sickness in transfer function equations using system identification (SI), by utilising black-box feature to use the experimental data as input and output to allow SI to predict the transfer function models. The aim of this study is to investigate MS factors in relation to the vehicle movement and occupant’s behavior, to develop multiple transfer function models, to analyze and compare them. The results were obtained in the three different transfer function orders, second, third and fourth order functions for each proportion used for both the driver and passenger, the driver models’ results were in between 64.68%-67.87%, and the passenger results were in between 63.75%-67.93%, after the comparison the highest fits for each order were obtained. The highest fits amongst driver models were 67.87% (4th Order), 66.78% (3rd Order) and 65.17% (2nd Order) and 67.93% (4th Order), 66.3% (3rd Order) and 64.82% (2nd Order) amongst the passenger models. Those fits were then validated via Simulink with unseen data that was not used in identification process, and lastly the models Root Mean Square Error (RMSE) was obtained for all of them to determine their efficiency.
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来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
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