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Multibody Dynamics with SymPy and PyDy

85bba1ca66eb909a289448a90e88f53a?s=47 Jim Crist
October 03, 2014

Multibody Dynamics with SymPy and PyDy

A talk I gave for my research lab about PyDy [1], SymPy [2], and my experience in Google Summer of Code [3].

Demo Notebook available on github: https://github.com/jcrist/talks/tree/master/pydy_talk.

[1] https://github.com/pydy/pydy
[2] https://github.com/sympy/sympy
[3] https://www.google-melange.com/gsoc/homepage/google/gsoc2014

85bba1ca66eb909a289448a90e88f53a?s=128

Jim Crist

October 03, 2014
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Transcript

  1. Multibody Dynamics with SymPy and PyDy Jim Crist (Or what

    I did this summer in 30 slides or less)
  2. What is SymPy??? • A Computer Algebra System (CAS) written

    in pure Python • Allows you to create expressions, and do things with them • SymPy knows about all the math needed
  3. Create Expressions >>> from sympy import * # Create Symbols

    >>> a, b, c = symbols('a, b, c') # Combine Symbols into expressions >>> expr = sin(a) + cos(b**2)*c >>> expr sin(a) + cos(b**2)*c
  4. Manipulate Expressions # subs performs substitutions on expressions >>> expr

    = expr.subs(b, a**(1/2)) >>> expr sin(a) + sin(a)*c # simplify reduces the size of expressions >>> expr = expr.simplify() >>> expr sin(a)*(1 + c)
  5. Solve Expressions # Create another symbol x >>> x =

    symbols('x') # Quadratic equation >>> lhs = a*x**2 + b*x + c >>> expr = Eq(lhs, 0) >>> expr a*x**2 + b*x + c == 0 # Solve returns solutions to expressions >>> solve(expr, x) [(-b + sqrt(-4*a*c + b**2))/(2*a), -(b + sqrt(-4*a*c + b**2))/(2*a)]
  6. Submodules (just a few…) • Calculus • Linear Algebra •

    Set Theory • Combinatorics • Differential Geometry • Tensors • Classical Mechanics • etc…
  7. Submodules (just a few…) • Calculus • Linear Algebra •

    Set Theory • Combinatorics • Differential Geometry • Tensors • Classical Mechanics • etc… What this talk is about
  8. Classical Mechanics • Describes the motion of macroscopic objects •

    Concerned with solving = = −1 ∙ • Many ways to do so: • Newton • Lagrange • Kane
  9. A VERY BRIEF OVERVIEW OF MULTIBODY DYNAMICS

  10. Vectors & Reference Frames

  11. Vectors & Reference Frames

  12. Generalized Coordinates & Speeds

  13. Constraints • If coordinates and speeds are nonminimal, need constraints

    • Holonomic • Relations between coordinates • 1 ,2 , 3 , … , , = 0 • Nonholonomic • “Not-holonomic” (i.e. cannot be put in form above)
  14. Constraints • Pendulum has 1 DOF, but 2 coordinates •

    Length is always • 1 , 2 , = 1 2 + 2 2 − 2 = 0
  15. Formulations • Lagrange • Enforces constraints with Lagrange Multipliers ()

    • Energy based formulation • Kane • Avoids need to compute energy equations • Uses generalized forces ( and ∗) • Can be simpler to compute for large systems
  16. Equations of Motion , = 0×1 , , = 0×1

    , , , = 0×1 , , , = 0×1 , , , ,, = 0 0−+ ×1 with , ∈ ℝ , ∈ ℝ ∈ ℝ ∈ ℝ
  17. Equations of Motion Can be rearranged to form: (, )

    = (, , , ,) or = , −1(, , ,, )
  18. Classical Mechanics • Inverted Pendulum, 1 Link

  19. Classical Mechanics • Inverted Pendulum, 3 Links: Rendered in size

    2.5 font
  20. DERIVE EQUATIONS IN SYMPY Solution:

  21. github.com/pydy/pydy

  22. Stuff I Did…

  23. Linearization • For systems with constraints, need to be careful

    • Can’t just take jacobian, as states are interdependent • i.e. 1 , , instead of 1 ()
  24. Example – Single Link Pendulum • Derived EOM for minimal,

    and nonminimal formulations
  25. Example – Single Link Pendulum Response for small angle deviation

    - simple jacobian linearization
  26. Example – Single Link Pendulum Response for small angle deviation

    - simple jacobian linearization Wrong…
  27. Linearization • Derived and implemented algorithm to robustly linearize all

    cases • Works for formulations from Kane or Lagrange • Handles holonomic and nonholonomic constraints • Applied to Whipple bicycle model [1], matches to 14 digits the benchmark eigenvalues [1] Meijaard, J.P., Papadopoulos, J.M., Ruina, A., Schwab, A.L.: Linearized dynamics equations for the balance and steer of a bicycle: a benchmark and review. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463(2084), 1955–1982(2007)
  28. Printers Symbolic Expression Fast Numeric Code Generators Wrappers • C

    • Fortran • Javascript • Mathematica • Theano • C • Fortran • Cython • F2Py • Numpy ufuncs Code Generation
  29. Other Stuff • Rewrote solver – 100x speed boost for

    (some) benchmarks • Improvements to substitution routines • Now () not (2) • Bug fixes for lots of things • Documentation
  30. Demo (try it at github.com/jcrist/talks/tree/master/pydy_talk)

  31. Why? • Open Source • Other symbolic solvers (AutoLev, SD/FAST)

    are proprietary • Fast • Slower generation than numeric methods • Faster simulation • Correct • No math errors for hand-calculated EOM • Automatic translation to C/Fortran -> no typing errors
  32. Future Work • O(n) articulated methods • Featherstone/Jain • Finish

    work on LAPACK/BLAS/ODEPACK integration • Will remove python layer bottleneck for simulation • Integration with CasADi