Reachability analysis of FMI models using data-driven dynamic sensitivity

Sergiy Bogomolov, Cláudio Gomes, Carlos Isasa, Sadegh Soudjani, Paulius Stankaitis, Thomas Wright
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Abstract

Digital twin is a technology that facilitates a real-time coupling of a cyber–physical system and its virtual representation. The technology is applicable to a variety of domains and facilitates more intelligent and dependable system design and operation, but it relies heavily on the existence of digital models that can be depended upon. In realistic systems, there is no single monolithic digital model of the system. Instead, the system is broken into subsystems, with models exported from different tools corresponding to each subsystem. In this paper, we focus on techniques that can be used for a black-box model, such as the ones implementing the Functional Mock-up Interface (FMI) standard, formal analysis, and verification. We propose two techniques for simulation-based reachability analysis of models. The first one is based on system dynamics, while the second one utilizes dynamic sensitivity analysis to improve the quality of the results. Our techniques employ simulations to obtain the model’s sensitivity with respect to the initial state (or model’s Lipschitz constant) which is then used to compute reachable states of the system. The approaches also provide probabilistic guarantees on the accuracy of the computed reachable sets that are based on simulations. Each technique requires different levels of information about the black-box system, allowing the readers to select the best technique according to the capabilities of the models. The validation experiments have demonstrated that our proposed algorithms compute accurate reachable sets of stable and unstable linear systems. The approach based on dynamic sensitivity provides an accurate and, with respect to system dimensions, more scalable approach, while the sampling-based method allows a flexible trade-off between accuracy and runtime cost. The validation results also show that our approaches are promising even when applied to nonlinear systems, especially, when applied to larger and more complex systems. The reproducibility package with code and data can be found at https://github.com/twright/FMI-Reachability-Reproducibility .
利用数据驱动的动态敏感性对 FMI 模型进行可达性分析
数字孪生是一种促进网络物理系统与其虚拟表示实时耦合的技术。该技术适用于各种领域,有助于提高系统设计和运行的智能性和可靠性,但它在很大程度上依赖于可依赖的数字模型的存在。在现实系统中,不存在单一的系统数字模型。相反,系统被分成若干子系统,每个子系统对应不同的工具输出模型。在本文中,我们将重点讨论可用于黑盒模型的技术,如实现功能模拟接口(FMI)标准、形式分析和验证的技术。我们提出了两种基于仿真的模型可达性分析技术。第一种基于系统动力学,第二种利用动态灵敏度分析来提高结果质量。我们的技术利用仿真来获得模型对初始状态(或模型的 Lipschitz 常量)的敏感性,然后用它来计算系统的可达状态。这些方法还为基于模拟计算的可达集的准确性提供概率保证。每种技术都需要不同程度的黑盒系统信息,读者可以根据模型的能力选择最佳技术。验证实验证明,我们提出的算法能准确计算稳定和不稳定线性系统的可达集。基于动态灵敏度的方法提供了一种准确的方法,而且就系统维度而言,更具可扩展性,而基于采样的方法则可以在准确性和运行时间成本之间灵活权衡。验证结果还表明,即使应用于非线性系统,特别是应用于更大型、更复杂的系统时,我们的方法也大有可为。包含代码和数据的可重现性软件包可在 https://github.com/twright/FMI-Reachability-Reproducibility 上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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