Algorithmic bisimulation for communicating piecewise deterministic Markov processes

In this paper we present an algorithm for finding a bisimulation relation for stochastic hybrid systems from the class of CPDPs (Communicating Piecewise Deterministic Markov Processes). We prove that the fixed point of the algorithm forms a bisimulation on the state space of the CPDP. We give sufficient conditions on the continuous dynamics and the transition structure of a CPDP, for the computation of the algorithm to be decidable

Compositional modelling of stochastic hybrid systems

In this thesis we present a modelling framework for compositional modelling of stochastic hybrid systems. Hybrid systems consist of a combination of continuous and discrete dynamics. The state space of a hybrid system is hybrid in the sense that it consists of a continuous component and a discrete component

Value passing for Communicating Piecewise Deterministic Markov Processes

In this paper we extend the CPDP model, which is used for compositional specification of PDP-type stochastic hybrid systems, to the value passing CPDP model. With value passing we can express communication of values of continuous variables between CPDP components. We show that the class of value passing CPDPs is closed under composition. We illustrate the use of value passing CPDPs by modelling an Air Traffic Management system as a network of interacting value passing CPDPs.