By John Fitzgerald, Peter Gorm Larsen, Marcel Verhoef
One of the main major demanding situations within the improvement of embedded and cyber-physical structures is the space among the disciplines of software program and keep watch over engineering. In a market, the place quick innovation is key, engineers from either disciplines have to be capable of discover procedure designs collaboratively, allocating tasks to software program and actual parts, and studying trade-offs among them.
To this finish, this booklet provides a framework that permits the very other kinds of layout types – discrete-event (DE) models of software program and continuous time (CT) versions of the actual setting – to be analyzed and simulated together, in line with universal eventualities. the person chapters supply introductions to either side of this co-simulation know-how, and provides a step by step consultant to the technique for designing and studying co-models. they're grouped into 3 components: half I introduces the technical foundation for collaborative modeling and simulation with the Crescendo expertise. half II maintains with diversified methodological directions for growing co-models and examining them in several methods utilizing case experiences. half III then delves into extra complex issues and appears into the capability way forward for this expertise within the zone of cyber-physical structures. eventually a variety of appendices offer summaries of the VDM and 20-sim applied sciences, a few useful layout styles acceptable for co-models, and an acronym checklist in addition to indices and references to different literature. by means of combining descriptions of the underlying idea with files of genuine engineers’ adventure in utilizing the framework on a sequence of case reviews the booklet appeals to scientists and practitioners alike. it truly is complemented via instruments, examples, video clips, and different fabric on www.crescendotool.org.
Scientists/researchers and graduate scholars operating in embedded and cyber-physical structures will research the semantic foundations for collaborative modeling and simulation, in addition to the present features and barriers of tools and instruments during this box. Practitioners could be in a position to advance an appreciation of the features of the co-modeling concepts, to evaluate some great benefits of extra collaborative ways to modeling and simulation, and should enjoy the integrated guidance and modeling patterns.
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Additional info for Collaborative Design for Embedded Systems: Co-modelling and Co-simulation
The focus is on developing the DE controller first. CT-first: In this approach, initial models are produced in the CT tool, with a DE model being introduced later to form a co-model. The focus is on modelling the dynamics of the plant. Contract-first: In this third approach, a contract is defined initially. The constituent models are then developed separately but concurrently, following the respective DE-first and CT-first approaches. The contract acts as a guide and target for constituent model development.
Software engineers with experience of object-oriented language should not find the move to VDM-RT difficult. Similarly, experience of other CT formalisms such as Matlab should permit a smooth transition to 20-sim for modellers. Note, however, than a team entirely composed of DE or CT experts should be careful not to be overly biased by their backgrounds. 3 Paths to Co-modelling Building a first co-model is a big step towards adopting our approach. We define three “standard” paths to reach a first co-model, which are based on the structure of a co-model.
For a co-model to produce simulation results that can be trusted, the DE and CT models must be consistent with each other. Consistency can be broken down into two parts. If the models agree on the identities and data types of the variables, parameters and events they share, then they can be said to be syntactically consistent with each other. Achieving syntactic consistency alone does not guarantee that the simulation will produce trustworthy results. For that, the models must also agree on the semantics of the variables, parameters and events they share.