Python ControlSystem.set_param 예제들

예제 #1

파일: ex5_Acrobot.py 프로젝트: and2345/pytrajectory

        0.0]

xb = [  0.0,
        0.0,
        1/2.0*np.pi,
        0.0]

# boundary values for the inputs
ua = [0.0]
ub = [0.0]

# create trajectory object
S = ControlSystem(f, a=0.0, b=2.0, xa=xa, xb=xb, ua=ua, ub=ub)

# alter some method parameters to increase performance
S.set_param('su', 10)

# run iteration
S.solve()


# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation

def draw(xti, image):
    phi1, phi2 = xti[0], xti[2]
    
    L=0.5

예제 #2

파일: ex3_Aircraft.py 프로젝트: and2345/pytrajectory

    return ff 

# system state boundary values for a = 0.0 [s] and b = 3.0 [s]
xa = [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
xb = [10.0, 0.0, 5.0, 0.0, 0.0, 0.0]

# boundary values for the inputs
ua = [0.5*9.81*50.0/(cos(5/360.0*2*pi)), 0.5*9.81*50.0/(cos(5/360.0*2*pi))]
ub = [0.5*9.81*50.0/(cos(5/360.0*2*pi)), 0.5*9.81*50.0/(cos(5/360.0*2*pi))]

# create trajectory object
S = ControlSystem(f, a=0.0, b=3.0, xa=xa, xb=xb, ua=ua, ub=ub)

# don't take advantage of the system structure (integrator chains)
# (this will result in a faster solution here)
S.set_param('use_chains', False)

# also alter some other method parameters to increase performance
S.set_param('kx', 5)

# run iteration
S.solve()

# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation

def draw(xti, image):
    x, y, theta = xti[0], xti[2], xti[4]

예제 #3

파일: ex5_Acrobot.py 프로젝트: and2345/pytrajectory

# system state boundary values for a = 0.0 [s] and b = 2.0 [s]
xa = [0.0, 0.0, 3 / 2.0 * np.pi, 0.0]

xb = [0.0, 0.0, 1 / 2.0 * np.pi, 0.0]

# boundary values for the inputs
ua = [0.0]
ub = [0.0]

# create trajectory object
S = ControlSystem(f, a=0.0, b=2.0, xa=xa, xb=xb, ua=ua, ub=ub)

# alter some method parameters to increase performance
S.set_param('su', 10)

# run iteration
S.solve()

# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation


def draw(xti, image):
    phi1, phi2 = xti[0], xti[2]

    L = 0.5

예제 #4

파일: ex3_Aircraft.py 프로젝트: and2345/pytrajectory

# boundary values for the inputs
ua = [
    0.5 * 9.81 * 50.0 / (cos(5 / 360.0 * 2 * pi)),
    0.5 * 9.81 * 50.0 / (cos(5 / 360.0 * 2 * pi))
]
ub = [
    0.5 * 9.81 * 50.0 / (cos(5 / 360.0 * 2 * pi)),
    0.5 * 9.81 * 50.0 / (cos(5 / 360.0 * 2 * pi))
]

# create trajectory object
S = ControlSystem(f, a=0.0, b=3.0, xa=xa, xb=xb, ua=ua, ub=ub)

# don't take advantage of the system structure (integrator chains)
# (this will result in a faster solution here)
S.set_param('use_chains', False)

# also alter some other method parameters to increase performance
S.set_param('kx', 5)

# run iteration
S.solve()

# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation


def draw(xti, image):

예제 #5

# system state boundary values for a = 0.0 [s] and b = 1.8 [s]
xa = [0.0, 0.0, 0.4 * np.pi, 0.0]

xb = [0.2 * np.pi, 0.0, 0.2 * np.pi, 0.0]

# boundary values for the inputs
ua = [0.0]
ub = [0.0]

# create trajectory object
S = ControlSystem(f, a=0.0, b=1.8, xa=xa, xb=xb, ua=ua, ub=ub)

# also alter some method parameters to increase performance
S.set_param('su', 20)
S.set_param('kx', 3)

# run iteration
S.solve()

# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation


def draw(xti, image):
    phi1, phi2 = xti[0], xti[2]

예제 #6

파일: ex2_InvertedDualPendulumSwingUp.py 프로젝트: and2345/pytrajectory

    return ff


# system state boundary values for a = 0.0 [s] and b = 2.0 [s]
xa = [0.0, 0.0, np.pi, 0.0, np.pi, 0.0]
xb = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

# boundary values for the input
ua = [0.0]
ub = [0.0]

# create trajectory object
S = ControlSystem(f, a=0.0, b=2.0, xa=xa, xb=xb, ua=ua, ub=ub)

# alter some method parameters to increase performance
S.set_param('su', 10)
S.set_param('eps', 8e-2)

# run iteration
S.solve()

# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation


def draw(xti, image):
    x, phi1, phi2 = xti[0], xti[2], xti[4]

예제 #7

파일: ex2_InvertedDualPendulumSwingUp.py 프로젝트: and2345/pytrajectory

    
	return ff

# system state boundary values for a = 0.0 [s] and b = 2.0 [s]
xa = [0.0, 0.0,  np.pi, 0.0,  np.pi, 0.0]
xb = [0.0, 0.0, 0.0, 0.0, 0.0, 0.0]

# boundary values for the input
ua = [0.0]
ub = [0.0]

# create trajectory object
S = ControlSystem(f, a=0.0, b=2.0, xa=xa, xb=xb, ua=ua, ub=ub)

# alter some method parameters to increase performance
S.set_param('su', 10)
S.set_param('eps', 8e-2)

# run iteration
S.solve()


# the following code provides an animation of the system above
# for a more detailed explanation have a look at the 'Visualisation' section in the documentation
import sys
import matplotlib as mpl
from pytrajectory.visualisation import Animation

def draw(xti, image):
    x, phi1, phi2 = xti[0], xti[2], xti[4]