Ramon R. H. Schiffelers

Syntax and consistent equation semantics of hybrid Chi

Abstract The hybrid χ (Chi) formalism integrates concepts from dynamics and control theory with concepts from computer science, in particular from process algebra and hybrid automata. It integrates ease of modeling with a straightforward, structured operational semantics. Its ‘consistent equation semantics’ enforces state changes to be consistent with delay predicates, that combine the invariant and flow clauses of hybrid automata. Ease of modeling is ensured by means of the following concepts: (1) different classes of variables: discrete and continuous, of subclass jumping or non-jumping, and algebraic; (2) strong time determinism of alternative composition in combination with delayable guards; (3) integration of urgent and non-urgent actions; (4) differential algebraic equations as a process term as in mathematics; (5) steady-state initialization; and 6) several user-friendly syntactic extensions. Furthermore, the χ formalism incorporates several concepts for complex system specification: (1) process terms for scoping that integrate abstraction, local variables, local channels and local recursion definitions; (2) process definition and instantiation that enable process re-use, encapsulation, hierarchical and/or modular composition of processes; and (3) different interaction mechanisms: handshake synchronization and synchronous communication that allow interaction between processes without sharing variables, and shared variables that enable modular composition of continuous-time or hybrid processes. The syntax and semantics are illustrated using several examples.

Concrete Syntax and Semantics of the Compositional Interchange Format for Hybrid Systems

Abstract The compositional interchange format for hybrid systems is syntactically and semantically defined in terms of an interchange automaton in an abstract format, allowing among others differential algebraic equations, variables that can be internal or external, operators for parallel composition, action hiding, variable hiding and urgent actions, synchronization by means of shared labels, and communication by means of shared variables and CSP channels. A concrete format is defined for modeling. Its semantics is defined in terms of a mapping to the abstract format. The concrete format adds inputs, outputs and open and closed scopes to enable modular and hierarchical specifications. The concrete format is illustrated by means of a bottle filling line example.

Formal Semantics of Hybrid Chi

The verification formalism / modeling and simulation language hybrid Chi is defined. The semantics of hybrid Chi is formally specified using Structured Operational Semantics (SOS) and a number of associated functions. The χ syntax and semantics can also deal with local scoping of variables and/or channels, implicit differential algebraic equations, such as higher index systems, and they are very well suited for specification of pure discrete event systems.

Foundations of a compositional interchange format for hybrid systems

A compositional interchange format for hybrid systems is defined in terms of an interchange automaton, allowing arbitrary differential algebraic equations, including fully implicit or switched DAEs, discrete, continuous and algebraic variables, that can be internal or external, urgency conditions, and operators for parallel composition, action hiding, variable hiding and urgent actions. Its compositional semantics is formally defined in terms of a hybrid transition system. This allows development of transformations to and from other formalisms that can be proven to preserve essential properties, and it allows a clear separation between the mathematical meaning of a model and implementation aspects such as algorithms used for solving differential algebraic equations.

Relating Hybrid Chi to Other Formalisms

The hybrid @g (Chi) formalism is suited to modeling, simulation and verification of hybrid systems. It integrates concepts from dynamics and control theory with concepts from computer science, in particular from process algebra and hybrid automata. In this paper, we first provide an overview of @g. Then, the @g formalism is related to other formalisms by means of translation schemes: a translation scheme from continuous-time PWA systems to @g, a translation scheme from discrete-time PWA systems to @g, and a translation scheme from hybrid automata to @g. In order to be able to use existing model checkers that use hybrid automata like input languages, we developed and implemented a translation scheme from a subset of @g to hybrid automata. To illustrate this approach, a case study has been performed: a water level monitor has been modeled using @g. Using the implemented translation scheme from @g to hybrid automata, we obtain a hybrid automata model for the water level monitor. From this model, code that can be used as input for the model checker PHAVer is generated.

Exploiting Specification Modularity to Prune the Optimization-Space of Manufacturing Systems

In this paper we address the makespan optimization of industrial-sized manufacturing systems. We introduce a framework which specifies functional system requirements in a compositional way and automatically computes makespan optimal solutions respecting these requirements. We show the optimization problem to be NP-Hard. To scale towards systems of industrial complexity, we propose a novel approach based on a subclass of compositional requirements which we call constraints. We prove that these constraints always prune the worst-case optimization-space thereby increasing the odds of finding an optimal solution (with respect to the additional constraints). We demonstrate the applicability of the framework on an industrial-sized manufacturing system.

Model-Based Software Engineering: A Multiple-Case Study on Challenges and Development Efforts

A recurring theme in discussions about the adoption of Model-Based Engineering (MBE) is its effectiveness. This is because there is a lack of empirical assessment of the processes and (tool-)use of MBE in practice. We conducted a multiple-case study by observing 2 two-month MBE projects from which software for a Mars rover were developed. We focused on assessing the distribution of the total software development effort over different development activities. Moreover, we observed and collected challenges reported by the developers during the execution of projects. We found that the majority of the effort is spent on the collaboration and communication activities. Furthermore, our inquiry into challenges showed that tool-related challenges are the most encountered.

Towards Automated Analysis of Model-Driven Artifacts in Industry.

Developing complex (sub)systems is a multi-disciplinary activity resulting in several, complementary models, possibly on different abstraction levels. The relations between all these models are usually loosely defined in terms of informal documents. It is not uncommon that only till the moment of integration at implementation level, shortcomings or misunderstanding between the different disciplines is revealed. In order to keep models consistent and to reason about multiple models, the relations between models have to be formalized. MultiDisciplinary System Engineering (MDSE) ecosystems provide a means for this. These ecosystems formalize the domain of interest using Domain Specific Languages (DSLs), and formalize the relations between models by means of automated model transformations. This enables consistency checking between domain and aspect models and facilitates multi-disciplinary analysis of the single (sub)system at hand. MDSE ecosystems provide the means to analyze a single (sub)system model. A set of models of different (sub)systems can be analyzed to derive best modeling practices and modeling patterns, and to measure whether a MDSE ecosystem fulfills its needs. The MDSE ecosystem itself can be instrumented to analyze how the MDSE ecosystem is used in practice. The evolution of models, DSLs and complete MDSE ecosystems is studied to identify and develop means that support evolution at minimal costs while maintaining high quality. In this paper, we present the anatomy of MDSE ecosystems with industrial examples, the ongoing work to enable the various types of analysis, each with their dedicated purpose. We conclude with a number of future research directions.