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A Composable Simulation Environment for Mechatronic Systems

A Composable Simulation Environment for Mechatronic Systems. Antonio Diaz-Calderon Christiaan J. J. Paredis Pradeep K. Khosla Carnegie Mellon University Work sponsored by DARPA RaDEO program and CMU 1999 European Simulation Symposium October 26-28, 1999, Erlangen, Germany. Hydraulic

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A Composable Simulation Environment for Mechatronic Systems

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  1. A Composable Simulation Environment for Mechatronic Systems Antonio Diaz-CalderonChristiaan J. J. Paredis Pradeep K. Khosla Carnegie Mellon University Work sponsored by DARPA RaDEO program and CMU 1999 European Simulation Symposium October 26-28, 1999, Erlangen, Germany.

  2. Hydraulic cylinder Controller Control signal Reference Actuation mechanism Flaps Flaps Motivation CAD Simulation Simulation-based Design 80% design verification By designers

  3. Composable Simulation: Overview • A New Modeling Paradigm • Integration with CAD • Differential Algebraic Equation Solving

  4. A New Modeling Paradigm • Modeling with system components • Reconfigurable models • Automatic model selection

  5. Designer Modeling with System Components • Composition of components + Interactions

  6. Configuration of model classes Set of interaction rules Defines a family of models Model realization Electsystem Conv system Mechsystem Reconfigurable Models Electric Motor

  7. Reconfigurable Models DC Motor • Two principles: • Model composition • Model instantiation • Hierarchical • Models containmeta-knowledge • Operating conditions • Compatibility constraints • Approximations • Semantics • … ModelComposition Electmodel Conv system Mechmodel Series + leakage nofriction Series ModelInstantiation Series + core losses

  8. Experiment Model Experiment Experiment Model Phys. Syst. Phys. Syst. Sim. Cost Model Selection • Ideal: • Model Validity Model Efficiency Find the least expensive model that is still valid for the given experiment

  9. Integration With CAD • User-friendly interface • familiar to designers • very rich information • Extract lumped parameters • kinematics • inertial properties • thermal interaction • electro-magnetic interaction

  10. Integration with CAD Kinematic Analysis Extraction of lumped parameters Mechanics Model

  11. Model Compilation System-Level Simulation Composable Simulation Approach System Description Component Interfaces System Composition Model Fragments Model Selection Selection Criteria Software Components

  12. Model Compilation • System graph approach • Intermediate representation capturing energy flow in system • Ascend • Solving DAEs • Object oriented modeling

  13. Terminal variables Terminal graph a x,y + b + Two-terminal B A y x System Graph Approach x(t) = f(y(t)) • Based on work by Trent, Branin, and Roe

  14. Topological Constraints • Basic postulates [Roe]: 1) Ay = 0 Kirchhoff current law 2) Bx = 0 Kirchhoff voltage law

  15. System Graph for 3D Mechanics Kinematic Analysis Extraction of lumped parameters

  16. System Graph for 3D Mechanics • Knowledge-dependent reduction • Avoid index problems Extended system graph • Identify composite bodies • Extract + combine inertial parameters • Remove redundant joints Reduced system graph

  17. System Graph for Non-mechanical Domain A Terminal graphs C5 p1 R2 R3 B R4 C5 g7 R6 R4 System graph R2 R3 D C E R2 d R3 c e R4 R6 + g7(t) p1(t) p1 b g7 G H F C5 R6 a, f, g, h

  18. TerminalEquations NodeEquations LoopEquations Dynamic Equations • Causality assignment • Terminal Equations: • d/dt (primary) = f (secondary) • Node + Loop Equations • secondary = g (primary) • Result • d/dt (primary) = f (g (primary))

  19. Causality Assignment • Normal tree: • Defines primary (p) and secondary variables (s) • Properties • Minimum cost spanning tree algorithm • Weighted system graph • classification based on form of terminal equation • Kruskal algorithm

  20. Low Power Component Modeling • Hybrid model representation: • Block diagrams (signals) • System graph • Variable elements • Signal-controlled across or through driver • X(t) = f(t) • Y(t) = h(t) • Fixed causality

  21. Low Power Component Modeling • Unknowns: state derivatives and algebraic variables • Software component: x states y u

  22. Low Power Component Modeling • Classification of a software component based on fo(l) • algebraic • non-algebraic • Augmented system of equations and

  23. Simulation Compute BLT schedule Evaluate BLT schedule Integration step Evaluate derivatives

  24. Pitch Motor Pitch Control Signal Reference PID Mechanical System Control Signal Reference PID Yaw Yaw Motor Example: Missile Seeker Enhanced CAD model • 2 DOFs • 3 energy domains: • Mechanical • Electrical • Signal • 17-part assembly • PID controllers and position sensors • Electric motors and signal amplifiers

  25. System Editor

  26. Augmented System Graph

  27. Summary • Composable simulation • A new modeling paradigm • For simulation of mechatronic systems • Approach: • System graph: single representation across domains • Integration with CAD

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