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Graphical Modeling of Hybrid Dynamics with Simulink and Stateflow

Published:11 April 2018Publication History

ABSTRACT

Simulink and Stateflow are tools for Model-Based Design that support a variety of mechanisms for modeling hybrid dynamics. Each of these tools has different strengths. In this paper, a new modeling construct is presented that combines these strengths to enable graphical modeling of hybrid dynamics within a single Stateflow chart. A new type of Stateflow state that acts as a Simulink subsystem is developed to facilitate graphical modeling of continuous dynamics using Simulink blocks inside Stateflow. Remote textual and graphical state access using new state-accessor blocks enables continuous states to be used in transition guards and reset actions. Key features of this new formalism are illustrated using various examples with hybrid dynamics.

References

  1. Building a clutch lock-up model. http://www.mathworks.com/help/simulink/examples/building-a-clutch-lock-up-model.html.Google ScholarGoogle Scholar
  2. Execution order for parallel states. http://www.mathworks.com/help/stateflow/ug/execution-order-for-parallel-states.html.Google ScholarGoogle Scholar
  3. Modeling a bouncing ball. http://www.mathworks.com/help/stateflow/examples/modeling-a-bouncing-ball.html.Google ScholarGoogle Scholar
  4. Simulation of a bouncing ball. http://www.mathworks.com/help/simulink/examples/simulation-of-a-bouncing-ball.html.Google ScholarGoogle Scholar
  5. State decomposition in stateflow. https://www.mathworks.com/help/stateflow/ug/state-decomposition.html.Google ScholarGoogle Scholar
  6. Yo-yo control of satellites. http://www.mathworks.com/help/stateflow/examples/yo-yo-control-of-satellites.html.Google ScholarGoogle Scholar
  7. D. Artis, B. Heggestad, C. Krupiarz, M. Mirantes, and J. Reid. Messenger: Flight software design for a deep space mission. In 2007 IEEE Aerospace Conference, pages 1--9, March 2007.Google ScholarGoogle ScholarCross RefCross Ref
  8. T. Chen, M. Diciolla, M. Kwiatkowska, and A. Mereacre. A simulink hybrid heart model for quantitative verification of cardiac pacemakers. In Proc. of HSCC 2013, pages 131--136. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. A. Chutinan and B. H. Krogh. Computational techniques for hybrid system verification. IEEE Transactions on Automatic Control, 48(1):64--75.Google ScholarGoogle Scholar
  10. A. Donzé, B. Krogh, and A. Rajhans. Parameter synthesis for hybrid systems with an application to simulink models. In R. Majumdar and P. Tabuada, editors, HSCC 2009, volume 5469 of LNCS, pages 165--179. Springer Berlin Heidelberg. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. C. Fan, P. S. Duggirala, S. Mitra, and M. Vishwanathan. Progress on powertrain contrl verification challenge with C2E2. In Applied Verification for Continuous and Hybrid Systems workshop (ARCH), 2015.Google ScholarGoogle Scholar
  12. Z. Han, P. Mosterman, J. Zander, and F. Zhang. Systematic management of simulation state for multi-branch simulations in Simulink. In Proc. of the Symposium on Theory of Modeling and Simulation (TMS) 2013, pages 84--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. C. Jackson and J. R. Henry. Orion GN&C model based development: Experience and lessons learned.Google ScholarGoogle Scholar
  14. X. Jin, J. V. Deshmukh, J. Kapinski, K. Ueda, and K. Butts. Powertrain contrl verification benchmark. In Proc. of HSCC 2014, pages 253--262. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. T. Johnson, S. Bak, and S. Drager. Cyber-physical specification mismatch identification with dynamic analysis. In Proc. of ICCPS 2015, pages 208--217. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. G. Rouleau. Olympic 2016 - pole vault. https://blogs.mathworks.com/simulink/2016/08/19/olympic-2016-pole-vault/, 2016.Google ScholarGoogle Scholar
  17. F. Zhang, M. Yeddanapudi, and P. Mosterman. Zero-crossing location and detection algorithms for hybrid system simulation. In 17th IFAC World Congress, pages 7967--7972, 2008.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    HSCC '18: Proceedings of the 21st International Conference on Hybrid Systems: Computation and Control (part of CPS Week)
    April 2018
    296 pages
    ISBN:9781450356428
    DOI:10.1145/3178126

    Copyright © 2018 ACM

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 11 April 2018

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    Overall Acceptance Rate153of373submissions,41%

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