Disciplined Heterogeneous Modeling

Lee, Edward A.

doi:10.1007/978-3-642-16129-2_20

Cited by 28 publications

(20 citation statements)

References 37 publications

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“…Connections between objects are introduced by connectequations in the equation part of a class." [20] Modelica uses hybrid differential algebraic equations as mathematical model and has it strength in modeling and simulation of complex and large physical systems [19]. Therefore, it is used by many system engineers to develop physically realistic models.…”

Section: Real-time Coordination Modelica Librarymentioning

confidence: 99%

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A Modelica Coordination Pattern Library for Cyber-Physical Systems

Pohlmann

Dziwok

Meyer

et al. 2014

Proceedings of the Seventh International Conference on Simulation Tools and Techniques

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Today's embedded systems often do not operate individually anymore. Instead, they form so called cyber-physical systems, where the overall functionality is provided by the collaboration of systems. Consequently, the design of this collaboration is an important activity during development and strongly affects system quality. In previous work, we presented a catalog of reusable messagebased real-time coordination patterns to avoid manual creation of new and, thus, error-prone designs. In this paper, we present an implementation of this catalog by a library in the Modelica language and an appropriate development process. The library stores ready to reuse solutions for common coordination activities and, thus, increases efficiency and effectiveness for use. Furthermore, the use of Modelica enables early holistic simulation of cyber-physical systems including feedback controllers and message-based coordination. We illustrate the library with examples from an autonomous railway vehicle and present an early evaluation.

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Section: Real-time Coordination Modelica Librarymentioning

confidence: 99%

“…Further, Ptolemy only supports continuous time in form of ordinary differential equations. In contrast to Modelica that "has significant advantages, particularly for specifying physical models based on differential-algebraic equations" [19].…”

Section: Related Workmentioning

confidence: 99%

A Modelica Coordination Pattern Library for Cyber-Physical Systems

Pohlmann

Dziwok

Meyer

et al. 2014

Proceedings of the Seventh International Conference on Simulation Tools and Techniques

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“…Implementers of tools are free to interpret this intent, resulting in a modeling language whose semantics is defined by the tools rather than by the language itself. There are many semantic alternatives [41], consequently the same SysML diagram may be interpreted very differently by different observers. MARTE (Modeling and Analysis of Real-Time and Embedded systems) [60] also specifically avoids "constraining" (or even defining) the execution semantics of models.…”

Section: The State Of the Artmentioning

confidence: 99%

Addressing Modeling Challenges in Cyber-Physical Systems

Derler¹,

Lee²,

Sangiovanni‐Vincentelli³

2011

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. This paper focuses on the challenges of modeling cyber-physical systems that arise from the intrinsic heterogeneity, concurrency, and sensitivity to timing of such systems. It uses a portion of an aircraft vehicle management systems (VMS), specifically the fuel management subsystem, to illustrate the challenges and then discusses technologies that at least partially address the challenges. Specific technologies described include hybrid system modeling and simulation, concurrent and heterogeneous models of computation, the use of domain-specific ontologies to enhance modularity, and the joint modeling of functionality and implementation architectures. Copyright © 2011, by the author(s).All rights reserved.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. AcknowledgementThis work was supported in part by the Center for Hybrid and Embedded Software Systems (CHESS) at UC Berkeley, which receives support from the National Science Foundation (NSF awards #0720882 (CSR-EHS: PRET), #0931843 (ActionWebs), and #1035672 (CSR-CPS Ptides)), the U. S. Army Research Office (ARO #W911NF-07-2-0019), the U. Abstract This paper focuses on the challenges of modeling cyber-physical systems that arise from the intrinsic heterogeneity, concurrency, and sensitivity to timing of such systems. It uses a portion of an aircraft vehicle management systems (VMS), specifically the fuel management subsystem, to illustrate the challenges, and then discusses technologies that at least partially address the challenges. Specific technologies described include hybrid system modeling and simulation, concurrent and heterogeneous models of computation, the use of domain-specific ontologies to enhance modularity, and the joint modeling of functionality and implementation architectures. 1

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“…The modeling and simulation tool Ptolemy 7 has been a pioneering, long-term, and on-going effort to provide a solution to the model composition problem in the presence of heterogeneity (Eker et al, 2003;Lee, 2010). Ptolemy follows the actor-oriented paradigm, where a system consists of a set of actors, which can be seen as processes executing concurrently and communicating using some mechanism.…”

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confidence: 99%

A modular formal semantics for Ptolemy

Tripakis¹,

Stergiou²,

Shaver³

et al. 2013

Math. Struct. Comp. Sci.

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Ptolemy is an open-source and extensible modeling and simulation framework. It offers heterogeneous modeling capabilities by allowing different models of computation, both untimed and timed, to be composed hierarchically in an arbitrary fashion. This paper proposes a formal semantics for Ptolemy which is modular, in the sense that atomic actors and their compositions are treated in a unified way. In particular, all actors conform to an executable interface that contains four functions: fire (produce outputs given current state and inputs), postfire (update state instantaneously), deadline (how much time the actor is willing to let elapse) and time-update (update state with passage of time). Composite actors are obtained from composition operators that in Ptolemy are called directors. Different directors realize different models of computation. This paper defines formally the directors for the following models of computation: Synchronous-Reactive, Discrete Event, Continuous Time, Process Networks, and Modal Models.

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Disciplined Heterogeneous Modeling

Cited by 28 publications

References 37 publications

A Modelica Coordination Pattern Library for Cyber-Physical Systems

A Modelica Coordination Pattern Library for Cyber-Physical Systems

Addressing Modeling Challenges in Cyber-Physical Systems

A modular formal semantics for Ptolemy

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