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Acumen A Cyber-Physical (CPS) Modeling Language Rigorous Simulation Walid Taha Halmstad University and Rice University Rigorous Simulation Aaron Ames, Kevin Atkinson , Jerker Bengtsson, Raktim Bhattacharya, Paul Brauner, Robert Cartwright,


  1. Acumen A Cyber-Physical (CPS) Modeling Language Rigorous Simulation Walid Taha Halmstad University and Rice University

  2. Rigorous Simulation Aaron Ames, Kevin Atkinson , Jerker Bengtsson, Raktim Bhattacharya, Paul Brauner, Robert Cartwright, Alexandre Chapoutot, Adam Duracz , Jan Duracz , Henrik Eriksson, Veronica Gaspes, Christian Grante, Jun Inoue, Michal Konecny , Marcie O’Malley, Travis Martin,Jawad Masood, Marisa Peralta, Cherif Salama, Walid Taha , Edwin Westbrook, Fei Xu, Yingfu Zeng , Yun Zhu Halmstad University, Rice University and Texas A&M, SP, Volvo Trucks

  3. Robot Design

  4. NG-Test Project

  5. Simulation in innovation Idea Model Prototype Product

  6. Simulation in innovation Idea Bug Model Flaw Prototype Disaster Product

  7. Rigorous Simulation Debugging models (simulation) Idea is a productivity bottleneck Test Bug Model CPS models must Simulate combine C & P! Flaw Prototype Disaster Product Late “debugging” can be extremely expensive

  8. This talk • The CPS simulation domain • Available tools • Rigorous simulation • The staging connection • The E-L equation (previous work) • Binding time analysis (ongoing)

  9. Modeling Simulation

  10. Why simulation is hard • Solving continuous equations: • ODEs, DAEs, PDEs, IDEs, ... • IVP vs. BVP • Hybrid aspects pervasive (both C & Ph) • Dealing with precision in models • Uncertainty (intervals)

  11. Hybrid systems basics Derivatives Interaction Discontinuities Computability

  12. Specific problems • Equality & zero crossing (if x=0 then ...) • Zeno-behavior (bouncing ball) • Static verification (e.g. solvable, stable) • Numerical precision and validity • Pole detection (e.g. x’=x*x, x(0)=1(?)) • Semantic treatment desperately needed

  13. Current tools • Simulink • Mathematica, Maple • ML, Haskell • HA, e.g. CHARON • Modelica, Verilog-AMS, ...

  14. Strengths • Simulink: Lots of models (IP) • Mathematica, Maple: Symbolics • ML, Haskell: Expressivity, concurrency • HA, e.g. CHARON: Formal proof • Modelica, Verilog-AMS: Equations

  15. Weaknesses • Simulink: Numeric semantics... • Mathematica: Executability • ML, Haskell: “ Indeterminism” • HA, e.g. CHARON: Scalability • Modelica, Verilog-AMS: Semantics...

  16. Acumen • Acumen ’09: continuous language • “Math as a programming language” • Acumen ’10 (- now): hybrid language • Hierarchical hybrid systems • IDE (automatic plotting & 3D view) • Rigorous semantics

  17. Basic Look and Feel

  18. Hybrid Dynamics Example

  19. Example for CPS course

  20. Rigorous Semantics E t x t x’ t

  21. Rigorous Semantics

  22. The Staging Connection A passive robot walks Can this be naturally down inclines generalized?

  23. The Staging Connection • Effort by one of our three domain experts • Robot model uses 8x8 Lagrangian • Must convert to “executable” math • Mathematica gave a 13MB derivative! • It’s very hard for robotics experts to find the right tools for simulation

  24. Equational models

  25. Equational models

  26. Equational models

  27. Equational models

  28. Equational models

  29. Equational models

  30. Equational models

  31. From Acumen to DAEs

  32. From Acumen to DAEs

  33. Effect on Performance Acumen ’09 [ICCPS’10]

  34. BTA: Syntax

  35. BTA: Collecting constraints

  36. BTA: Solving constraints

  37. Example: Pendulum

  38. Example: Pendulum

  39. Example: Pendulum

  40. BTA: Next Steps • Formalizing correctness criteria • “Well-annotated programs don’t go wrong” • Establishing sufficiency for an interesting class of models • Example: Rigid body dynamics

  41. Conclusions (1/2) • Rigorous simulation is a powerful tool • Being based on simulation makes intuitive • Being rigorous makes it a verification tool • Semantics makes the tool rigorous • Staging implements the semantics efficiently

  42. Conclusions (2/2) • Rigorous simulation naturally accommodates parametric uncertainty • Modeling uncertainty makes simulations much more informative • Using rigorous simulation during early-stage design has a distinctive flavor that promotes robust design

  43. Thank you! • To download our papers: • http://effective-modeling.org • To download Acumen • http://acumen-language.org • For CPS Lecture Notes • http://bit.ly/LNCPS-2014

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