wC1,
wK0,
wK2,
v0=v0,
vf=vf,
num=num)
w, v = control.eig_of_series(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# arm model (mean)
aAs, aBs, aSpeed = cbi.mean_arm(allRiders)
indices = np.int32(np.round(speeds * 10))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues, aEigenvalues)
rlfig.set_size_inches(5., 5. / goldenRatio)
rlfig.savefig('../../figures/systemidentification/A-A-eig.png')
rlfig.savefig('../../figures/systemidentification/A-A-eig.pdf')
# Root locus with respect to speed.
v0 = 0.
vf = 10.
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM,
iC1,
iK0,
iK2,
v0=v0,
vf=vf,
num=num)
def plot(canon, H, riders, environments, idMats):
filename = ''
for rider in riders:
filename += '-' + rider
for env in environments:
filename += '-' + env.replace(' ', '')
filename = '../plots/' + filename[1:]
print filename
v0 = 0.
vf = 10.
num = 100
# identified
iM, iC1, iK0, iK2, iH = idMats
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
v0=v0, vf=vf, num=num)
w, v = control.eigen_vs_parameter(iAs)
iEigenvalues, iEigenvectors = control.sort_modes(w, v)
# whipple model (mean)
wM, wC1, wK0, wK2, wH = cbi.mean_canon(riders, canon, H)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
v0=v0, vf=vf, num=num)
w, v = control.eigen_vs_parameter(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# arm model (mean)
aAs, aBs, aSpeed = cbi.mean_arm(riders)
indices = np.int32(np.round(speeds * 10))
w, v = control.eigen_vs_parameter(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
# eigenvalue plot
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues,
aEigenvalues)
rlfig.savefig(filename + '-eig.png')
# root locus
v0 = 0.
vf = 10.
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
v0=v0, vf=vf, num=num)
iEig, null = control.eigen_vs_parameter(iAs)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
v0=v0, vf=vf, num=num)
wEig, null = control.eigen_vs_parameter(wAs)
indices = np.int32(np.round(speeds * 10))
aEig, null = control.eigen_vs_parameter(aAs[indices])
rlcfig = cbi.plot_rlocus(speeds, iEig, wEig, aEig)
rlcfig.savefig(filename + '-rlocus.png')
# bode plots
speeds = np.array([2.0, 4.0, 6.0, 9.0])
null, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
speeds)
null, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
speeds)
figs = cbi.plot_bode(speeds, iAs, iBs, wAs, wBs, aAs, aBs)
figs[0].savefig(filename + '-Tphi.png')
figs[1].savefig(filename + '-Tdel.png')
wM, wC1, wK0, wK2 = cbi.load_benchmark_canon(['Luke'])['Luke']
wH = cbi.lateral_force_contribution(['Luke'])['Luke']
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
v0=v0, vf=vf,
num=num)
w, v = control.eig_of_series(wAs)
wEigenvalues, wEigenvectors = control.sort_modes(w, v)
# Load the Arm model state space for each rider from first principles and
# generate the eigenvalues and eigenvectors as a function of speed.
aAs, aBs, aSpeed = cbi.mean_arm(['Luke'])
indices = np.int32(np.round(speeds * 10))
w, v = control.eig_of_series(aAs[indices])
aEigenvalues, aEigenvectors = control.sort_modes(w, v)
rlfig = cbi.plot_rlocus_parts(speeds, iEigenvalues, wEigenvalues,
aEigenvalues)
rlfig.axes[0].set_ylabel('')
rlfig.set_size_inches(4.0, 4.0 / goldenRatio)
plt.tight_layout()
rlfig.savefig('../figures/L-P-eig.pdf')
# Root locus with respect to speed.
v0 = 0.
vf = 10.
num = 20
speeds, iAs, iBs = bicycle.benchmark_state_space_vs_speed(iM, iC1, iK0, iK2,
v0=v0, vf=vf,
num=num)
iEig, null = control.eig_of_series(iAs)
speeds, wAs, wBs = bicycle.benchmark_state_space_vs_speed(wM, wC1, wK0, wK2,
