论文标题
网络物理系统的概率一致性
Probabilistic Conformance for Cyber-Physical Systems
论文作者
论文摘要
在系统分析中，一致性表明两个系统同时满足相同的关注规格。因此，分析一个系统会自动传输到另一个系统的结果，或者一个系统可以在实践中安全替换另一个系统。在这项工作中，我们研究了网络物理系统（CPS）的概率统一。我们提出了一个（近似）概率统一的概念，该概念是信号时间逻辑（STL）表达的一组复杂规格集的概念。基于新的统计检验，我们开发了针对一类CP的概率统一的第一个统计验证方法。使用这种方法，我们验证了丰田动力总成系统的广泛使用和简化模型的启动时间的一致性，Toyota动力总成系统，基于模型的基于模型的基于基于神经网络的模型对照和基于神经网络的自动驾驶汽车保存控制器的定居时间，以及完全和简化电网系统的最大电压偏差。
In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatically transfer to the other, or one system can safely replace the other in practice. In this work, we study the probabilistic conformance of cyber-physical systems (CPS). We propose a notion of (approximate) probabilistic conformance for sets of complex specifications expressed by the Signal Temporal Logic (STL). Based on a novel statistical test, we develop the first statistical verification methods for the probabilistic conformance of a wide class of CPS. Using this method, we verify the conformance of the startup time of the widely-used full and simplified model of Toyota powertrain systems, the settling time of model-predictive-control-based and neural-network-based automotive lane-keeping controllers, as well as the maximal voltage deviation of full and simplified power grid systems.
