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Continuous to discrete transformations for state-space and transfer function.
# Author: Jeffrey Armstrong <>
# March 29, 2011
import numpy as np
from scipy import linalg
from ltisys import tf2ss, ss2tf, zpk2ss, ss2zpk
__all__ = ['cont2discrete']
def cont2discrete(sys, dt, method="zoh", alpha=None):
"""Transform a continuous to a discrete state-space system.
sys : a tuple describing the system.
The following gives the number of elements in the tuple and
the interpretation:
* 2: (num, den)
* 3: (zeros, poles, gain)
* 4: (A, B, C, D)
dt : float
The discretization time step.
method : {"gbt", "bilinear", "euler", "backward_diff", "zoh"}
Which method to use:
* gbt: generalized bilinear transformation
* bilinear: Tustin's approximation ("gbt" with alpha=0.5)
* euler: Euler (or forward differencing) method ("gbt" with
* backward_diff: Backwards differencing ("gbt" with alpha=1.0)
* zoh: zero-order hold (default).
alpha : float within [0, 1]
The generalized bilinear transformation weighting parameter, which
should only be specified with method="gbt", and is ignored otherwise
sysd : tuple containing the discrete system
Based on the input type, the output will be of the form
(num, den, dt) for transfer function input
(zeros, poles, gain, dt) for zeros-poles-gain input
(A, B, C, D, dt) for state-space system input
By default, the routine uses a Zero-Order Hold (zoh) method to perform
the transformation. Alternatively, a generalized bilinear transformation
may be used, which includes the common Tustin's bilinear approximation,
an Euler's method technique, or a backwards differencing technique.
The Zero-Order Hold (zoh) method is based on [1]_, the generalized bilinear
approximation is based on [2]_ and [3].
.. [1]
.. [2]
.. [3] G. Zhang, X. Chen, and T. Chen, Digital redesign via the generalized
bilinear transformation, Int. J. Control, vol. 82, no. 4, pp. 741-754,
2009. (
if len(sys) == 2:
sysd = cont2discrete(tf2ss(sys[0], sys[1]), dt, method=method,
return ss2tf(sysd[0], sysd[1], sysd[2], sysd[3]) + (dt,)
elif len(sys) == 3:
sysd = cont2discrete(zpk2ss(sys[0], sys[1], sys[2]), dt, method=method,
return ss2zpk(sysd[0], sysd[1], sysd[2], sysd[3]) + (dt,)
elif len(sys) == 4:
a, b, c, d = sys
raise ValueError("First argument must either be a tuple of 2 (tf), "
"3 (zpk), or 4 (ss) arrays.")
if method == 'gbt':
if alpha is None:
raise ValueError("Alpha parameter must be specified for the "
"generalized bilinear transform (gbt) method")
elif alpha < 0 or alpha > 1:
raise ValueError("Alpha parameter must be within the interval "
"[0,1] for the gbt method")
if method == 'gbt':
# This parameter is used repeatedly - compute once here
ima = np.eye(a.shape[0]) - alpha*dt*a
ad = linalg.solve(ima, np.eye(a.shape[0]) + (1.0-alpha)*dt*a)
bd = linalg.solve(ima, dt*b)
# Similarly solve for the output equation matrices
cd = linalg.solve(ima.transpose(), c.transpose())
cd = cd.transpose()
dd = d + alpha*, bd)
elif method == 'bilinear' or method == 'tustin':
return cont2discrete(sys, dt, method="gbt", alpha=0.5)
elif method == 'euler' or method == 'forward_diff':
return cont2discrete(sys, dt, method="gbt", alpha=0.0)
elif method == 'backward_diff':
return cont2discrete(sys, dt, method="gbt", alpha=1.0)
elif method == 'zoh':
# Build an exponential matrix
em_upper = np.hstack((a, b))
# Need to stack zeros under the a and b matrices
em_lower = np.hstack((np.zeros((b.shape[1], a.shape[0])),
np.zeros((b.shape[1], b.shape[1])) ))
em = np.vstack((em_upper, em_lower))
ms = linalg.expm(dt * em)
# Dispose of the lower rows
ms = ms[:a.shape[0], :]
ad = ms[:, 0:a.shape[1]]
bd = ms[:, a.shape[1]:]
cd = c
dd = d
raise ValueError("Unknown transformation method '%s'" % method)
return ad, bd, cd, dd, dt
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