In the fields of co-simulation and component-based modelling, designers import models as building blocks to create a composite model that provides more complex functionalities. Modelling tools perform instantaneous cycle detection (ICD) on the composite models having feedback loops to reject the models if the loops are mathematically unsound and to improve simulation performance. In this case, the analysis relies heavily on the availability of dependency information from the imported models. However, the cycle detection problem becomes harder when the model’s input to output dependencies are mode-dependent, i.e. changes for certain events generated internally or externally as inputs. The number of possible modes created by composing such models increases significantly and unknown factors such as environmental inputs make the offline (statical) ICD a difficult task. In this paper, an online ICD method is introduced to address this issue for the models used in cyber–physical systems. The method utilises an oracle as a central source of information that can answer whether the individual models can make mode transition without creating instantaneous cycles. The oracle utilises three types of data-structures created offline that are adaptively chosen during online (runtime) depending on the frequency as well as the number of models that make mode transitions. During the analysis, the models used online are stalled from running, resulting in the discrepancy with the physical system. The objective is to detect an absence of the instantaneous cycle while minimising the stall time of the model simulation that is induced from the analysis. The benchmark results show that our method is an adequate alternative to the offline analysis methods and significantly reduces the analysis time.

在协同仿真和基于组件的建模领域，设计人员将模型作为构建模块导入，以创建提供更复杂功能的复合模型。建模工具会对具有反馈回路的复合模型执行瞬时周期检测（ICD），以在数学上不合理的情况下拒绝这些回路并提高仿真性能。在这种情况下，分析在很大程度上依赖于导入模型中依赖项信息的可用性。但是，当模型的输入到输出依存关系依赖于模式时，即对于内部或外部作为输入生成的某些事件发生变化时，周期检测问题将变得更加棘手。通过组合此类模型创建的可能模式的数量显着增加，并且诸如环境输入之类的未知因素使离线（静态）ICD成为一项艰巨的任务。在本文中，针对网络物理系统中使用的模型，引入了一种在线ICD方法来解决此问题。该方法利用预言作为主要信息源，可以回答各个模型是否可以在不创建瞬时周期的情况下进行模式转换。oracle使用离线创建的三种类型的数据结构，这些类型在联机（运行时）期间根据频率以及进行模式转换的模型数量进行自适应选择。在分析过程中，在线使用的模型停止运行，从而导致与物理系统的差异。目的是检测瞬时循环的缺失，同时最小化由分析引起的模型仿真的停顿时间。基准测试结果表明，我们的方法是脱机分析方法的适当替代方法，并且大大减少了分析时间。