CORA 2023中Reachset一致性的检查和建立

Matthias Althoff
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引用次数: 0

摘要

工具展示:当正式验证网络物理系统的模型时,显然重要的是,它们的验证结果可以转移到建模系统的所有先前观察中。我们的工具CORA通过检查真实系统的所有测量是否位于相应模型的可达输出集合中,从而使转移安全属性成为可能——我们称之为到达集一致性检查。此外,我们提供了通过在模型中注入不确定性来建立目标集一致性的策略。这可以看作是某种形式的系统识别,我们不是寻找最可能的参数,而是计算一组参数值——不仅针对模型动力学,还针对扰动和测量误差集——以建立达到集一致性。通过用高保真模型的模拟结果代替实际测量,还可以检查高保真模型是否符合简单模型。我们通过几个用例演示了达成集一致性的有用性。
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Checking and Establishing Reachset Conformance in CORA 2023
Tool presentation: When formally verifying models of cyber-physical systems, it is obviously important that their verification results can be transferred to all previous observations of the modeled systems. Our tool CORA makes it possible to transfer safety properties by checking whether all measurements of the real system lie in the set of reachable outputs of the corresponding model -- we call this reachset conformance checking. In addition, we provide strategies to establish reachset conformance by injecting nondeterminism in models. This can be seen as some form of system identification, where instead of finding the most likely parameters, we compute a set of parameter values -- not only for the model dynamics but also for the set of disturbances and measurement errors -- to establish reachset conformance. By replacing real measurements with simulation results from a high-fidelity model, one can also check whether a high-fidelity model conforms to a simple model. We demonstrate the usefulness of reachset conformance by several use cases.
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ARCH-COMP23 Category Report: Hybrid Systems Theorem Proving ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Linear Continuous Dynamics ARCH-COMP23 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics ARCH-COMP23 Repeatability Evaluation Report ARCH-COMP23 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants
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