Improved Co-Simulation with Event Detection for Stochastic Behaviors of CPSs

Cyber-Physical Systems (CPSs), inevitably exposed in open environment, are considered to be complex to analyze in terms of its intrinsic heterogeneity and potential stochastic behaviors. Some existing technologies like Functional Mockup Interface (FMI) could mitigate the issue to some extent, however there are still some challenging problems, for example, effective and efficient co-simulation for stochastic models like markov chains. To facilitate co-simulation of stochastic CPSs, we present an improved co-simulation framework that focuses on the capture of nearest future event to reduce the number of running steps and the frequency of data exchange between models. The core implementation is an adaptive co-simulator that integrates two algorithms optimized with event detection for co-simulation between Functional Mock-up Unit (FMU) and two different types of markov chains: DTMC and CTMC. Meanwhile, a Prism wrapper is implemented for interpreting a markov chain as a fake FMU. To demonstrate the ability of our improved co-simulation, we study two extended Bouncing Ball cases respectively modelled by DTMC and CTMC. The experiment result turns out that our approach is effective in generating simulation traces of stochastic CPSs and the optimized algorithms are more efficient compared with original one.