AdvancingModel-Based Design by Modeling Approximations of Computational Semantics

Pieter J. Mosterman
Design Automation Department, MathWorks, USA / School of Computer Science, McGill University, Canada

Justyna Zander
Harvard Humanitarian Initiative, Harvard University, USA

Ladda ner artikel

Ingår i: Proceedings of the 4th International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools; Zurich; Switzerland; September 5; 2011

Linköping Electronic Conference Proceedings 56:1, s. 3-7

Visa mer +

Publicerad: 2011-11-03

ISBN: 978-91-7519-825-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Over the past decades; engineered systems have increasingly come to rely on embedded computation in order to include advanced and sophisticated features. The unparallelled flexibility of software has been a blessing for implementing functionality with a complexity that could not have been imagined heretofore. One important manifestation of this is in the use of software as the universal system integration mechanism. With the increasing use; however; has come a suite of difficulties in effectively employing software engineering practices because (i) C (the language of choice in embedded software implementation) is very close to the hardware implementation and (ii) software engineering methods typically only consider logical correctness; irrespective of critical characteristics for embedded computation (e.g.; response time). To address these problems; Model-Based Design helps raise the level of abstraction while accounting for such critical characteristics. The corresponding models are designed using high-level formalisms such as block diagrams and state transition diagrams whose meaning is particularly intuitive because of their executable nature. The necessity to support increasingly complicated language elements; however; has caused the underlying execution engine to explode in complexity. As a result; the meaning of the high-level formalisms exists almost exclusively by merit of simulation. This paper attempts to present the challenges faced by the current state of Model-Based Design tools and outlines a solution approach by modeling the execution engine.


Model-Based Design; Cyber-Physical Systems; Modeling; Simulation; Computation; Numerical Integration; Hybrid Systems


[1] Karl J. Åström and BjörnWittenmark. Computer Controlled Systems: Theory and Design. Prentice-Hall; Englewood Cliffs; New Jersey; 1984.

[2] John Backus. Can programming be liberated from the von neumann style? a functional style and its algebra of programs. Communications of the ACM; 21(8):613–641; 1978.

[3] Maria Domenica Di Benedetto and Alberto L. Sangiovanni- Vincentelli; editors. Hybrid Systems: Computation and Control; volume 2034 of Lecture Notes in Computer Science. Springer-Verlag; March 2001.

[4] Albert Benveniste; Paul Caspi; Stephen A. Edwards; Nicolas Halbwachs; Paul Le Guernic; and Robert de Simone. The synchronous languages twelve years later. Proceedings of mthe IEEE; 91(1):64–83; 2003.

[5] Ben Denckla and Pieter J. Mosterman. Stream- and state-based semantics of hierarchy in block diagrams. In Proceedings of the 17th IFAC World Congress; pages 7955– 7960; Seoul; Korea; July 2008.

[6] Nicolas Halbwachs; Pascal Raymond; and Christophe Ratel. Generating efficient code from data-flow programs. In Third International Symposium on Programming Language Implementation and Logic Programming; Passau; Germany; August 1991.

[7] Derek J. Hatley and Imtiaz Pirbhai. Strategies for Real-Time Systems Specification. Dorset House Publishing Co.; New York; New York; 1988.

[8] High Confidence Software and Systems Coordinating Group. High-confidence medical devices: Cyber-physical systems for the 21st century health care. Technical report; Networking and Information Technology Research and Development Program; feb 2009.

[9] Jerry Krasner. Comparing embedded design outcomes with and without model-based design. Technical report; Embedded Market Forecasters; Framingham; MA; October 2010.

[10] Edward A. Lee. What’s ahead for embedded software. Computer; 33(9):18–26; September 2000.

[11] Joy Lin. Measuring return on investment of model-based design. EE Times Design; May 2011.

[12] Nancy Lynch and Bruce Krogh; editors. Hybrid Systems:Computation and Control; volume 1790 of Lecture Notes in Computer Science. Springer-Verlag; March 2000.

[13] Cleve Moler. Are we there yet? zero crossing and event handling for differential equations. EE Times; pages 16–17; 1997. Simulink 2 Special Edition.

[14] Pieter J. Mosterman. HYBRSIM—a modeling and simulation environment for hybrid bond graphs. Journal of Systems and Control Engineering; 216(1):35–46; 2002.

[15] Pieter J. Mosterman; Janos Sztipanovits; and Sebastian Engell. Computer automated multi-paradigm modeling in control systems technology. IEEE Transactions on Control System Technology; 12(2):223–234; March 2004.

[16] Pieter J. Mosterman and Hans Vangheluwe. Guest editorial: Special issue on computer automated multi-paradigm modeling. ACM Transactions on Modeling and Computer Simulation; 12(4):249–255; 2002.

[17] Pieter J. Mosterman and Hans Vangheluwe. Computer automated multi-paradigm modeling: An introduction. SIMULATION: Transactions of The Society for Modeling and Simulation International; 80(9):433–450; September 2004.

[18] Pieter J. Mosterman; Justyna Zander; Gregoire Hamon;and Ben Denckla. Towards computational hybrid system semantics for time-based block diagrams. In Proceedings of the 3rd IFAC Conference on Analysis and Design of Hybrid Systems; pages 376–385; Zaragoza; Spain; September 2009. plenary paper.

[19] Pieter J. Mosterman; Justyna Zander; Gregoire Hamon; and Ben Denckla. A computational model of time for stiff hybrid systems applied to control synthesis. Control Engineering Practice; 19; 2011.

[20] Gabriela Nicolescu and Pieter J. Mosterman; editors. Model-Based Design for Embedded Systems. Computational Analysis; Synthesis; and Design of Dynamic Systems. CRC Press; Boca Raton; FL; 2009. ISBN: 9781420067842.

[21] Linda R. Petzold. A description of DASSL: A differential/algebraic system solver. Technical Report SAND82- 8637; Sandia National Laboratories; Livermore; CA; 1982.

[22] Ingo Sander. System Modeling and Design Refinement in ForSyDe. PhD thesis; Royal Institute of Technology; Stockholm; Sweden; April 2003.

[23] Semiconductor Industry Association. International technology roadmap for semiconductors: 1999 edition—design. Technical report; Sematech; Austin; TX; 1999.

[24] Simulink R . Using Simulink R . MathWorks R ; Natick; MA; March 2011.

[25] Michael Sullivan. TACTICAL AIRCRAFT–F/A-22 and JSF acquisition plans and implications for tactical aircraft modernization. Technical Report GAO-05-519T; United States Government Accountability Office; April 2005.

[26] Justyna Zander; Pieter J. Mosterman; Gréegoire Hamon; and Ben Denckla. On the structure of time in computational semantics of a variable-step solver for hybrid behavior analysis. In Proceedings of the 18th IFAC World Congress; Milan; Italy; September 2011.

Citeringar i Crossref