Model-Based Systems Engineering Tool-Chain for Automated Parameter Value Selection

被引:10
|
作者
Lu, Jinzhi [1 ]
Chen, Dejiu [2 ]
Wang, Guoxin [3 ]
Kiritsis, Dimitris [1 ]
Torngren, Martin [2 ]
机构
[1] Ecole Polytech Fed Lausanne, SCI Dept, CH-1015 Lausanne, Switzerland
[2] KTH Royal Inst Technol, Dept Machine Design, S-10044 Stockholm, Sweden
[3] Beijing Inst Technol, Dept Mech Engn, Beijing 100081, Peoples R China
基金
欧盟地平线“2020”;
关键词
Artificial intelligence; Decision making; Modeling; Tools; Data models; Unified modeling language; Stakeholders; Automated parameter value selection; cyber-physical systems (CPSs); model-based systems engineering (MBSE); IOT;
D O I
10.1109/TSMC.2020.3048821
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cyber-physical systems (CPSs) integrate heterogeneous systems and process sensor data using digital services. As the complexity of CPS increases, it becomes more challenging to efficiently formalize the integrated multidomain views with flexible automated verification across the entire lifecycle. This article illustrates a model-based systems engineering tool-chain to support CPS development with an emphasis on automated parameter value selection for co-simulation. First, a domain-specific modeling approach is introduced to support the formalizations of CPS artifacts, development processes, and simulation configurations. The domain-specific models are used as the basis to generate a Web-based process management system for automated parameter value selections, which coordinates Open Services for Lifecycle Collaboration services of development information and technical resources (models, data, and tools) in order to support automated co-simulation. The services are deployed by a service orchestrator based on a decision-making algorithm for parameter value selection. Finally, developers make use of the WPMS to implement simulations and to select system parameter values for co-simulation automatically. The approach is illustrated by a case study on auto-braking system development and we evaluate the efficiency of this tool-chain by both qualitative and quantitative methods. The results show that parameter values are selected more efficiently and effectively when implementing co-simulations using our tool-chain.
引用
收藏
页码:2333 / 2347
页数:15
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