Bridging the semantic gap between heterogeneous modeling formalisms and FMI

Tripakis, Stavros

doi:10.1109/samos.2015.7363660

Cited by 45 publications

(40 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tripakis [25] shows how components with different underlying models (state machines, synchronous data flow, and so on) can be encoded as FMUs. Savicks [24] presents a framework for co-simulation of Event-B and continuous models based on FMI, using a fixedstep master algorithm and a characterisation of simulation components as a class specialised by Event-B models or FMUs.…”

Section: Discussionmentioning

confidence: 99%

Behavioural Models for FMI Co-simulations

Cavalcanti

Woodcock

Amálio

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Simulation is a favoured technique for analysis of cyberphysical systems. With their increase in complexity, co-simulation, which involves the coordinated use of heterogeneous models and tools, has become widespread. An industry standard, FMI, has been developed to support orchestration; we provide the first behavioural semantics of FMI. We use the state-rich process algebra, Circus, to present our modelling approach, and indicate how models can be automatically generated from a description of the individual simulations and their dependencies. We illustrate the work using three algorithms for orchestration. A stateless version of the models can be verified using model checking via translation to CSP. With that, we can prove important properties of these algorithms, like termination and determinism, for example. We also show that the example provided in the FMI standard is not a valid algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

Behavioural Models for FMI Co-simulations

Cavalcanti

Woodcock

Amálio

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Followup work includes [8], which proposes a collection of test cases together with acceptance criteria that can be used to determine whether a hybrid co-simulation technique is acceptable. Tripakis [47] investigates techniques to bridge the semantic gap between various formalisms (state machines, discrete-event models, dataflow, etc.) and FMI.…”

Section: Related Workmentioning

confidence: 99%

Hybrid co-simulation: it’s about time

et al. 2017

Self Cite

View full text Add to dashboard Cite

Model-based design methodologies are commonly used in industry for the development of complex cyber-physical systems (CPSs). There are many different languages, tools, and formalisms for model-based design, each with its strengths and weaknesses. Instead of accepting some weaknesses of a particular tool, an alternative Communicated by Prof. J. Sztipanovits, M. Broy, and H. Daembkes. This work is partially based on previous work published by the authors [7,8,15] is to embrace heterogeneity, and to develop tool integration platforms and protocols to leverage the strengths from different environments. A fairly recent attempt in this direction is the functional mock-up interface (FMI) standard that includes support for co-simulation. Although this standard has reached acceptance in industry, it provides only limited support for simulating systems that mix continuous and discrete behavior, which are typical of CPS. This paper identifies the representation of time as a key problem, because the FMI representation does not support well the discrete events that typically occur at the cyber-physical boundary. We analyze alternatives for representing time in hybrid co-simulation and conclude that a superdense model of time using integers only solves many of these problems. We show how an execution engine can pick an adequate time resolution, and how disparities between time representations internal to co-simulated components and the resulting effects of time quantization can be managed. We propose a concrete extension to the FMI standard for supporting hybrid co-simulation that includes integer time, automatic choice of time resolution, and the use of absent signals. We explain how these extensions can be implemented modularly within the frameworks of existing simulation environments.

show abstract

“…The behavior of individual FMUs is provided by the model-checker's simulation engines based on the guidelines described by Tripakis (2015). In particular, the report distinguishes continuous and discrete dynamics.…”

Section: Translating Models Into Fmusmentioning

confidence: 99%

“…Transitions can also be decorated with event labels and each tool supports its own kind(s) of synchronizing compositions internally and therefore the discrete transition synchronization is also handled individually within the tools. Tripakis (2015) provides the means of discrete transition synchronization by allocating two special port variables: one for incoming (input) synchronization and one for outgoing (output) synchronization. The domain of discrete input (output) ports coincides with the set of input (output) labels plus a special value absent which denotes no synchronization or an internal discrete transition.…”

Section: Translating Models Into Fmusmentioning

confidence: 99%

Co-Simulation of Hybrid Systems with SpaceEx and Uppaal

Bogomolov

Greitschus

Jensen

et al. 2015

Linköping Electronic Conference Proceedings

Self Cite

View full text Add to dashboard Cite

The Functional Mock-up Interface (FMI) is an industry standard which enables co-simulation of complex heterogeneous systems using multiple simulation engines. In this paper, we show how to use FMI in order to co-simulate hybrid systems modeled in the model checkers SPACEEX and UPPAAL. We show how FMI components can be automatically generated from SPACEEX and UPPAAL models. We also validate the cosimulation approach by comparing the simulations of a room heating benchmark in two cases: first, when a single model is simulated in SPACEEX; and second, when the model is split in two submodels, and co-simulated using SPACEEX and UPPAAL. Finally, we perform a measurement experiment on a composite model to show a potential for statistical model checking using stochastic co-simulations.

show abstract

Bridging the semantic gap between heterogeneous modeling formalisms and FMI

Cited by 45 publications

References 17 publications

Behavioural Models for FMI Co-simulations

Behavioural Models for FMI Co-simulations

Hybrid co-simulation: it’s about time

Co-Simulation of Hybrid Systems with SpaceEx and Uppaal

Contact Info

Product

Resources

About