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Real-time Simulation

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Continuous System Simulation

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In this chapter, we shall discuss the special requirements of real-time simulation, i.e., of simulation runs that keep abreast of the passing of real time, and that can accommodate driving functions (input signals) that are generated outside the computer and that are read in by means of analog to digital (A/D) converters.

Until now, computing speed has always been a soft constraint — slow simulation meant expensive simulation, but now, it becomes a very hard constraint. Simulation becomes a race against time. If we cannot complete the computations associated with one integration step before the real-time clock has advanced by h time units, where h is the current step size of the integration algorithm, the simulation is out of sync, and we just lost the race.

Until now, we always tried to make simulation more comfortable for the user. For example, we introduced step-size controlled algorithms so that the user wouldn’t have to worry any more about whether or not the numerical integration meets his or her accuracy requirements. The algorithm would do so on its own. In the context of real-time simulation, we may not be able to afford all this comfort any longer. We may have to throw many of the more advanced features of simulation over board in the interest of saving time, but of course, this means that we have to understand even better ourselves how simulation works in reality.

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10.12 References

  1. Jeff R. Cash. A Semi-implicit Runge-Kutta Formula for the Integration of Stiff Systems of Ordinary Differential Equations. Chemical Engineering J., 20(3):219–224, 1980.

    Article  MathSciNet  Google Scholar 

  2. François E. Cellier, Larry C. Schooley, Malur K. Sundareshan, and Bernard P. Zeigler. Computer-aided Design of Intelligent Controllers: Challenge of the Nineties. In Recent Advances in Computer Aided Control Systems Engineering, pages 53–80, Amsterdam, the Netherlands, 1992. Elsevier Science Publishers.

    Google Scholar 

  3. François E. Cellier, Larry C. Schooley, Bernard P. Zeigler, Adele Doser, Glenn Farrenkopf, JinWoo Kim, YaDung Pan, and Brian Willams. Watchdog Monitor Prevents Martian Oxygen Production Plant from Shutting Itself Down During Storm. In Proceedings ISRAM’92, ASME Conference on Intelligent Systems for Robotics and Manufacturing, pages 697–704, Santa Fe, N.M., 1992.

    Google Scholar 

  4. François E. Cellier. Continuous System Modeling. Springer Verlag, New York, 1991. 755p.

    Google Scholar 

  5. François E. Cellier. Inlining Step-size Controlled Fully Implicit Runge-Kutta Algorithms for the Semi-analytical and Semi-numerical Solution of Stiff ODEs and DAEs. In Proceedings 5thConference on Computer Simulation, pages 259–262, Mexico City, Mexico, 2000.

    Google Scholar 

  6. Richard J. Charron and Min Hu. A-contractivity of Linearly Implicit Multistep Methods. SIAM Journal on Numerical Analysis, 32(1):285–295, 1995.

    Article  MathSciNet  Google Scholar 

  7. Christoph Clauss, Hilding Elmqvist, Sven Erik Mattsson, Martin Otter, and Peter Schwarz. Mixed Domain Modeling in Modelica. In Proceedings FDL’02, Forum on Specification and Design Languages, Marseille, France, 2002.

    Google Scholar 

  8. Rémi Cozot. From Multibody Systems Modeling to Distributed Real-Time Simulation. In Proceedings Simulation’96 IEEE Conference, pages 234–241, 1996.

    Google Scholar 

  9. Judith S. Dahmann, Frederick Kuhl, and Richard Weatherly. Standards for Simulation: As Simple As Possible But Not Simpler — The High Level Architecture For Simulation. Simulation, 71(6):378–387, 1998.

    Google Scholar 

  10. Judith S. Dahmann. The High Level Architecture and Beyond: Technology Challenges. In Proceedings PADS’99, 13thWorkshop on Parallel and Distributed Simulation, pages 64–70, Atlanta, Georgia, 1999.

    Google Scholar 

  11. Álvaro de Albornoz Bueno and François E. Cellier. Qualitative Simulation Applied to Reason Inductively About the Behavior of a Quantitatively Simulated Aircraft Model. In Proceedings QUARDET’93, IMACS International Workshop on Qualitative Reasoning and Decision Technologies, pages 711–721, Barcelona, Spain, 1993.

    Google Scholar 

  12. Álvaro de Albornoz Bueno and François E. Cellier. Variable Selection and Sensor Fusion in Automatic Hierarchical Fault Monitoring of Large Scale Systems. In Proceedings QUARDET’93, IMACS International Workshop on Qualitative Reasoning and Decision Technologies, pages 722–734, Barcelona, Spain, 1993.

    Google Scholar 

  13. John R. Dormand and Peter J. Prince. Runge-Kutta Triples. J. of Computational and Applied Mathematics, 12A(9):1007–1017, 1986.

    Article  MathSciNet  Google Scholar 

  14. Eduard Eitelberg. Modellreduktion linearer zeitinvarianter Systeme durch Minimieren des Gleichungsfehlers. PhD thesis, University of Karlsruhe, Karlsruhe, Germany, 1979.

    Google Scholar 

  15. Hilding Elmqvist, Sven Erik Mattsson, and Hans Olsson. New Methods for Hardware-in-the-loop Simulation of Stiff Models. In Proceedings Modelica’2002 Conference, pages 59–64, Oberpfaffenhofen, Germany, 2002.

    Google Scholar 

  16. Hilding Elmqvist, Martin Otter, and François E. Cellier. Inline Integration: A New Mixed Symbolic/Numeric Approach for Solving Differential-Algebraic Equation Systems. In Proceedings European Simulation Multiconference, pages xxiii–xxxiv, Prague, Czech Republic, 1995.

    Google Scholar 

  17. Hilding Elmqvist and Martin Otter. Methods for Tearing Systems of Equations in Object-oriented Modeling. In Proceedings European Simulation Multiconference, pages 326–332, Barcelona, Spain, 1994.

    Google Scholar 

  18. Javier Garcia de Jalón and Eduardo Bayo. Kinematic and Dynamic Simulation of Multibody Systems — The Real-Time Challenge-. Wiley, 1994.

    Google Scholar 

  19. Kjell Gustafsson and Gustaf Söderlind. Control Strategies for the Iterative Solution of Nonlinear Equations in ODE Solvers. SIAM Journal on Scientific Computing, 18(1):23–40, 1997.

    Article  MathSciNet  Google Scholar 

  20. Frank Hodum and David Edwards. Time Management Services in the RTI-NG. In Proceedings SIW’01, Fall Simulation Interoperability Workshop, paper 01F-SIW-090, 2001.

    Google Scholar 

  21. Robert M. Howe and Kuo-Chin Lin. The Use of Function Generation in the Real-time Simulation of Stiff Systems. In AIAA Flight Simulation Technologies Conference and Exhibit, pages 217–224, Dayton, Ohio, 1990.

    Google Scholar 

  22. Robert M. Howe. The Use of Mixed Integration Algorithms in State Space. Transactions of the Society for Computer Simulation, 7(1):45–66, 1990.

    Google Scholar 

  23. Robert M. Howe. On-line Calculation of Dynamic Errors in Real-time Simulation. In Proceedings of SPIE, volume 3369, pages 31–42, 1998.

    Google Scholar 

  24. Robert M. Howe. Real-Time Multi-Rate Asynchronous Simulation with Single and Multiple Processors. In Proceedings of SPIE, volume 3369, pages 3–14, 1998.

    Google Scholar 

  25. Thomas Kailath. Linear Systems. Prentice-Hall, Englewood Cliffs, N.J., 1980.

    Google Scholar 

  26. Granino A. Korn and John V. Wait. Digital Continuous-System Simulation. Prentice-Hall, Englewood Cliffs, N.J., 1978.

    Google Scholar 

  27. Granino A. Korn. Interactive Dynamic-System Simulation. McGraw-Hill, New York, 1989.

    Google Scholar 

  28. Granino A. Korn. Neural-Network Experiments on Personal Computers. MIT Press, Cambridge, Mass., 1991.

    Google Scholar 

  29. Matthias Krebs. Modeling of Conditional Index Changes. Master’s thesis, Dept. of Electrical & Computer Engineering, University of Arizona, Tucson, Ariz., 1997.

    Google Scholar 

  30. John Laffitte and Robert M. Howe. Interfacing Fast and Slow Subsystems in the Real-time Simulation of Dynamic Systems. Transactions of SCS, 14(3):115–126, 1997.

    Google Scholar 

  31. Nicolas Léchevin, Camille Alain Rabbath, and Paul Baracos. Distributed Real-time Simulation of Power Systems Using Off-the-shelf Software. IEEE Canadian Review, pages 5–8, 2001. summer edition.

    Google Scholar 

  32. Kuo-Chin Lin and Robert M. Howe. Simulation Using Staggered Integration Steps — Part I: Intermediate-Step Predictor Methods. Transactions of the Society for Computer Simulation, 10(3):153–164, 1993.

    Google Scholar 

  33. Kuo-Chin Lin and Robert M. Howe. Simulation Using Staggered Integration Steps — Part II: Implementation on Dual-Speed Systems. Transactions of the Society for Computer Simulation, 10(4):285–297, 1993.

    Google Scholar 

  34. Francisco Mugica and François E. Cellier. Automated Synthesis of a Fuzzy Controller for Cargo Ship Steering by Means of Qualitative Simulation. In Proceedings ESM’94, European Simulation MultiConference, Barcelona, Spain, 1994.

    Google Scholar 

  35. Martin Otter, Hilding Elmqvist, and François E. Cellier. ‘Relaxing’ — A Symbolic Sparse Matrix Method Exploiting the Model Structure in Generating Efficient Simulation Code. In Proceedings Symposium on Modeling, Analysis, and Simulation, CESA’96, IMACS Multi-Conference on Computational Engineering in Systems Applications, volume 1, pages 1–12, Lille, France, 1996.

    Google Scholar 

  36. Olgierd A. Palusinski. Simulation of Dynamic Systems Using Multirate Integration Techniques. Transactions of SCS, 2(4):257–273, 1986.

    Google Scholar 

  37. José Ignacio Rodríguez, José Manuel Jiménez, Francisco Javier Funes, and Javier Garcia de Jalón. Dynamic Simulation of Multi-Body Systems on Internet Using CORBA, Java and XML. Multibody System Dynamics, 10(2):177–199, 2003.

    Article  Google Scholar 

  38. Anton Schiela and Folkmar Bornemann. Sparsing in Real Time Simulation. ZAMM, Zeitschrift für angewandte Mathematik und Mechanik, 83(10):637–647, 2003.

    Article  MathSciNet  Google Scholar 

  39. Anton Schiela and Hans Olsson. Mixed-mode Integration for Real-time Simulation. In Modelica Workshop 2000 Proceedings, pages 69–75, Lund, Sweden, 2000.

    Google Scholar 

  40. Anton Schiela. Sparsing in Real Time Simulation. Diploma Project, Technische Universität München, 2002. 75p.

    Google Scholar 

  41. Michael C. Schweisguth and François E. Cellier. A Bond Graph Model of the Bipolar Junction Transistor. In Proceedings SCS 4thInternational Conference on Bond Graph Modeling and Simulation, pages 344–349, San Francisco, California, 1999.

    Google Scholar 

  42. Siddhartha Shome. Dual-rate Integration Using Partitioned Runge-Kutta Methods for Mechanical Systems With Interacting Subsystems. PhD thesis, The University of Iowa, 2000.

    Google Scholar 

  43. Jon M. Smith. Mathematical Modeling and Digital Simulation for Engineers and Scientists. John Wiley & Sons, New York, second edition, 1987.

    Google Scholar 

  44. Simon J.E. Taylor, Jon Saville, and Rajeev Sudra. Developing Interest Management Techniques in Distributed Interactive Simulation Using Java. In Proceedings WSC’99, Winter Simulation Conference, pages 518–523, 1999.

    Google Scholar 

  45. Pentti J. Vesanterä and François E. Cellier. Building Intelligence into an Autopilot — Using Qualitative Simulation to Support Global Decision Making. Simulation, 52(3):111–121, 1989.

    Google Scholar 

  46. Jörg Wensch, Karl Strehmel, and Rüdiger Weiner. A Class of Linearly-Implicit Runge-Kutta Methods for Multibody Systems. Applied Numerical Mathematics, 22(1–3):381–398, 1996.

    Article  MathSciNet  Google Scholar 

  47. Bernard P. Zeigler, Larry C. Schooley, François E. Cellier, and FeiYue Wang. High-Autonomy Control of Space Resource Processing Plants. IEEE Control Systems, 13(3):29–39, 1993.

    Article  Google Scholar 

10.13 Bibliography

  1. Hilding Elmqvist, Sven Erik Mattsson, Hans Olsson, Johan Andreasson, Martin Otter, Christian Schweiger, and Brück, Dag. Real-time Simulation of Detailed Automotive Models. In Proceedings 2003 Modelica Conference, Linköping, Sweden, 2003.

    Google Scholar 

  2. José Manuel Jiménez Bascones. Formulaciones cinemáticas y dinámicas para la simulación en tiempo real de sistemas de sólidos rígidos. PhD thesis, Universidad de Navarra, San Sebastián, Spain, 1993.

    Google Scholar 

  3. Sean Murphy, Jonathan Labin, and Robert Lutz. Experiences Using the Six Services of the IEEE 1516.1 Specification: A 1516 Tutorial. In Proceedings SIW’04, Spring Simulation Interoperability Workshop, paper 04S-SIW-056, 2004.

    Google Scholar 

  4. Shinichi Soejima and Takashi Matsuba. Application of Mixed Mode Integration and New Implicit Inline Integration at Toyota. In Proceedings 2002 Modelica Conference, Oberpfaffenhofen, Germany, 2002.

    Google Scholar 

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(2006). Real-time Simulation. In: Continuous System Simulation. Springer, Boston, MA. https://doi.org/10.1007/0-387-30260-3_10

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  • DOI: https://doi.org/10.1007/0-387-30260-3_10

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