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#
# MIT No Attribution
#
# Copyright (C) 2010-2023 Joel Andersson, Joris Gillis, Moritz Diehl, KU Leuven.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this
# software and associated documentation files (the "Software"), to deal in the Software
# without restriction, including without limitation the rights to use, copy, modify,
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
# INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
# PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
# HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
# SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
#
# -*- coding: utf-8 -*-
from casadi import *
import pylab as plt
# Pendulum: point mass on massless rod
# x'' = T*x, y'' = T*y-g, x**2+y**2-L**2
#
#
# .
# L /
# /
# /
# o (x,y)
#
x = SX.sym("x") # horizontal position of point
y = SX.sym("y") # vertical position of point
dx = SX.sym("dx")
dy = SX.sym("dy")
u = SX.sym("u") # helper states to bring second order system to first order
v = SX.sym("v")
du = SX.sym("du")
dv = SX.sym("dv")
# Force
T = SX.sym("T")
L = 1
g = 9.81
alg = vertcat(dx-u,dy-v,du-T*x,dv-T*y+g,x**2+y**2-L**2)
# Implicit differential states
x_impl = vertcat(x,y,u,v)
# Derivatives of implict differential states
dx_impl = vertcat(dx,dy,du,dv)
z = T
dae = {"x_impl": x_impl, "dx_impl": dx_impl, "z": z, "alg": alg}
# Perform index reduction
(dae_reduced,stats) = dae_reduce_index(dae)
print("We had an index 3 system (reduced to index 1 now)")
print(stats)
print("Here's the reduced DAE:")
print(dae_reduced)
print("Notice how the algebraic equation now contains a second derivative of x**2+y**2-L**2")
print("Also note how x**2+y**2-L**2 and its first derivate are absent from the algebraic equations.")
print("Both are kept in 'I' though: the resulting DAE invariants.")
# The DAE is not yet in a form that CasADi integrator can handle (semi-explicit).
# Let's convert it, ad obtain some adaptors
[dae_se, state_to_orig, phi] = dae_map_semi_expl(dae, dae_reduced)
grid = list(np.linspace(0,5,1000))
tf = DM(grid).T
intg = integrator("intg","idas",dae_se,0,grid)
# Before we can integrate, we need a consistant initial guess
# Let's say we start at y=-0.5, dx=-0.1;
# consistency equations should automatically identify everything else
# Encode the desire to only enforce y and dx
init_strength = {}
free = 0 # default
force = -1
init_strength["x_impl"] = vertcat(free,force,free,free)
init_strength["dx_impl"] = vertcat(force,free,free,free)
init_strength["z"] = vertcat(free)
# Obtain a generating Function for initial guesses
init_gen = dae_init_gen(dae, dae_reduced, "ipopt", init_strength)
init = {}
blank = 0.0 # Will not be enforced but still used as initial guess
# suggest to initialize with the left-hanging solution by having x=-1 as initial guess
init["x_impl"] = vertcat(-1.0,-0.5,blank,blank)
init["dx_impl"] = vertcat(-0.1,blank,blank,blank)
init["z"] = blank
xz0 = init_gen(**init)
print("We have a consistent initial guess now to feed to the integrator:")
print(xz0)
print("Look, we found that force in the pendulum at t=0 equals:")
print(state_to_orig(xf=xz0['x0'],zf=xz0['z0'])["z"])
print("A consistent initial guess should make the invariants zero (up to solver precision):")
print(phi(x=xz0['x0'],z=xz0['z0']))
# Integrate and get resultant xf,zf on grid
sol = intg(**xz0)
print(sol['xf'].shape)
# Solution projected into original DAE space
sol_orig = state_to_orig(xf=sol["xf"],zf=sol["zf"])
# We can see the large-angle pendulum motion play out well in the u state
plt.plot(tf.T,sol_orig["x_impl"].T)
plt.grid(True)
plt.legend([e.name() for e in vertsplit(x_impl)])
plt.xlabel("Time [s]")
plt.title("Boundary value problem solution trajectory")
# A perfect integrator will perfectly preserve the values of the invariants over time
# Integrator errors make the invariants drift in practice
# This is not a detail; the pendulum length is in fact growing!
error = phi(x=sol["xf"],z=sol["zf"])["I"]
plt.figure()
plt.plot(tf.T,error.T)
plt.grid(True)
plt.xlabel("Time [s]")
plt.title("Evolution trajectory of invariants")
# There are some techniques to avoid the drifting of invariants associated with index reduction
# - Method of Dummy Derviatives (not implemented)
# hard implementation details: may need to switch between different choices online
# -> integration in parts triggered by zero-crossing events
# - Stop the integration every once in a while and project back (not implemented)
# - Baumgarte stabilization (implemented): build into the equations a form of continuous feedback that brings back deviations in invariants back into the origin
# Demonstrate Baumgarte stabilization with a pole of -1.
# Drift is absent now.
(dae_reduced,stats) = dae_reduce_index(dae, {"baumgarte_pole": -1})
[dae_se, state_to_orig, phi] = dae_map_semi_expl(dae, dae_reduced)
intg = integrator("intg","idas",dae_se,0,grid)
init_gen = dae_init_gen(dae, dae_reduced, "ipopt", init_strength)
xz0 = init_gen(**init)
sol = intg(**xz0)
error = phi(x=sol["xf"],z=sol["zf"])["I"]
plt.figure()
plt.plot(tf.T,error.T)
plt.grid(True)
plt.xlabel("Time [s]")
plt.title("Boundary value problem solution trajectory with Baumgarte pole=-1")
# Sweep across different integrator precisions and Baumgarte poles.
# The choice of pole is not really straightforward
plt.figure()
poles = [-0.1,-1,-10]
precisions = [0.1,1,10]
abstol_default = 1e-8
reltol_default = 1e-6
plt.subplot(len(precisions),len(poles),1)
for i,pole in enumerate(poles):
for j,precision in enumerate(precisions):
(dae_reduced,stats) = dae_reduce_index(dae, {"baumgarte_pole": pole})
[dae_se, state_to_orig, phi] = dae_map_semi_expl(dae, dae_reduced)
intg = integrator("intg","idas",dae_se,0,grid,{"abstol":abstol_default*precision,"reltol":reltol_default*precision})
init_gen = dae_init_gen(dae, dae_reduced, "ipopt", init_strength, {"ipopt.print_level":0,"print_time": False})
xz0 = init_gen(**init)
sol = intg(**xz0)
nfevals = intg.stats()["nfevals"]
error = phi(x=sol["xf"],z=sol["zf"])["I"]
plt.subplot(len(precisions),len(poles),j*len(poles)+i+1)
plt.plot(tf.T,error.T)
plt.grid(True)
plt.xlabel("Time [s]")
plt.title("pole=%0.1f, abstol=%0.0e => nfevals=%d" % (pole,abstol_default*precision,nfevals))
plt.show()
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