用影响概念提升对复杂系统仿真结果的理解。
A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems
- 引入‘影响’概念，捕捉未建模的环境交互。
- 通过迭代仿真改进对系统行为的认知。
- 适合参与多领域协同开发的工程师使用。
网络物理系统（CPS）通常由多个利益相关方开发，其成果物针对特定领域设计。系统行为源于这些成果物与运行环境的交互。仿真与联合仿真已成为分析CPS行为的关键方法，通过仿真实验可探索系统在不同条件下的响应，包括与环境的互动。然而，由于复杂性、时间或领域经验不足，部分环境介导的交互（特别是超出直接传感与执行的部分）缺乏建模细节，阻碍了对仿真结果的充分理解与利用。为解决此问题，本文提出一种概念框架，利用新提出的‘影响’概念，支持仿真实验的迭代增量优化，并深化对系统行为的理解。通过使用Simulink/Gazebo联合仿真的移动机器人案例研究验证了该方法的有效性。
原文摘要 · Abstract (English)
Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The behaviour of these systems emerges from the interaction between those artefacts and their operational environment. Simulation and co-simulation have become essential approaches for analysing CPS behaviour and, through simulation campaigns, developers can explore system responses under changing conditions, including interactions with the environment. However, the lack of details and understanding of some environmentmediated interactions (typically the ones beyond direct sensing and actuation), which remain unmodelled due to their complexity, a lack of time, or a lack of domain experience, hinders the proper comprehension and exploitation of simulation results. To address these limitations, we propose a conceptual framework leveraging the novel concept of Influences to support the iterative and incremental refinement of simulation campaigns and deepen the understanding of the system behaviour. We demonstrate the proposed approach through a case study involving a mobile robot implemented using Simulink/Gazebo co-simulation.
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