A permanently increasing number of on-board automotive control systems requires new approaches to their digital mapping that improves functionality in terms of adaptability and robustness as well as enables their easier on-line software update. As it can be concluded from many recent studies, various methods applying neural networks (NN) can be good candidates for relevant digital twin (DT) tools in automotive control system design, for example, for controller parameterization and condition monitoring. However, the NN-based DT has strong requirements to an adequate amount of data to be used in training and design. In this regard, the paper presents an approach, which demonstrates how the regression tasks can be efficiently handled by the modeling of a semi-active shock absorber within the DT framework. The approach is based on the adaptation of time series augmentation techniques to the stationary data that increases the variance of the latter. Such a solution gives a background to elaborate further data engineering methods for the data preparation of sophisticated databases.
翻译:不断增多的机载汽车控制系统要求采用新的数字制图方法,改善适应性和稳健性方面的功能,便于在线更新软件。正如最近许多研究得出的结论,在汽车控制系统设计中,应用神经网络(NN)的各种方法可以成为相关数字双星工具的良好候选工具,例如控制器参数化和条件监测。然而,基于NN的DT对培训和设计中应使用足够数量的数据提出了强烈的要求。在这方面,文件提出了一个方法,表明如何通过在DT框架内模拟半主动式电击吸收器来有效处理回归任务。这一方法的基础是将时间序列增强技术与增加后者差异的静止数据相适应。这种解决办法为进一步拟订数据编制精密数据库的数据工程方法提供了背景。