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Rework at step_info #293

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cf7885c
Fixing a minor error with some discrete-time systems
joaoantoniocardoso Apr 17, 2019
5538d96
Removing unneeded code.
joaoantoniocardoso Apr 17, 2019
712b16f
Resolution added for discrete-time systems.
joaoantoniocardoso Apr 17, 2019
90374b6
Fixing a possible mistake
joaoantoniocardoso Apr 17, 2019
d5e7895
Treating systems with negative step responses
joaoantoniocardoso Apr 17, 2019
e47ba8d
Treating systems with negative step responses
joaoantoniocardoso Apr 17, 2019
5f4f001
Merge pull request #1 from python-control/master
joaoantoniocardoso Apr 17, 2019
1ab6756
Merge branch 'master' of https://github.com/joaoantoniocardoso/python…
joaoantoniocardoso Apr 17, 2019
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35 changes: 18 additions & 17 deletions control/timeresp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -456,7 +456,7 @@ def step_response(sys, T=None, X0=0., input=None, output=None,
else:
# For discrete time, use integers
tvec = _default_response_times(sys.A, 100)
T = range(int(np.ceil(max(tvec))))
T = np.arange(tvec.max() / sys.dt) * sys.dt

U = np.ones_like(T)

Expand Down Expand Up @@ -509,42 +509,43 @@ def step_info(sys, T=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1,0.9))
>>> info = step_info(sys, T)
'''
sys = _get_ss_simo(sys)
if T is None:
if isctime(sys):
T = _default_response_times(sys.A, 1000)
else:
# For discrete time, use integers
tvec = _default_response_times(sys.A, 1000)
T = range(int(np.ceil(max(tvec))))

T, yout = step_response(sys, T)
yout = yout.flatten()

# Steady state value
InfValue = yout[-1]

# RiseTime
tr_lower_index = (np.where(yout >= RiseTimeLimits[0] * InfValue)[0])[0]
tr_upper_index = (np.where(yout >= RiseTimeLimits[1] * InfValue)[0])[0]
if InfValue >= 0:
tr_lower_index = (np.where(yout >= RiseTimeLimits[0] * InfValue)[0])[0]
tr_upper_index = (np.where(yout >= RiseTimeLimits[1] * InfValue)[0])[0]
else:
tr_lower_index = (np.where(yout <= RiseTimeLimits[0] * InfValue)[0])[0]
tr_upper_index = (np.where(yout <= RiseTimeLimits[1] * InfValue)[0])[0]
RiseTime = T[tr_upper_index] - T[tr_lower_index]

# SettlingTime
sup_margin = (1. + SettlingTimeThreshold) * InfValue
inf_margin = (1. - SettlingTimeThreshold) * InfValue
sup_margin = (1. + SettlingTimeThreshold * np.sign(InfValue)) * InfValue
inf_margin = (1. - SettlingTimeThreshold * np.sign(InfValue)) * InfValue
SettlingTime = 0
# find Steady State looking for the first point out of specified limits
for i in reversed(range(T.size)):
if((yout[i] <= inf_margin) | (yout[i] >= sup_margin)):
SettlingTime = T[i + 1]
if (yout[i] <= inf_margin) | (yout[i] >= sup_margin):
if T.size > i + 1:
i = i + 1
SettlingTime = T[i]
break

# Peak
PeakIndex = np.abs(yout).argmax()
PeakValue = yout[PeakIndex]
PeakValue = np.abs(yout[PeakIndex])
PeakTime = T[PeakIndex]
SettlingMax = (yout).max()
SettlingMax = (yout[tr_upper_index:]).max()
SettlingMin = (yout[tr_upper_index:]).min()
# I'm really not very confident about UnderShoot:
UnderShoot = yout.min()
OverShoot = 100. * (yout.max() - InfValue) / (InfValue - yout[0])
OverShoot = 100. * (np.sign(InfValue) * PeakValue - InfValue) / (InfValue - yout[0])

# Return as a dictionary
S = {
Expand Down