# scipy.signal.cont2discrete¶

scipy.signal.cont2discrete(system, dt, method='zoh', alpha=None)[source]

Transform a continuous to a discrete state-space system.

Parameters
systema tuple describing the system or an instance of lti

The following gives the number of elements in the tuple and the interpretation:

• 1: (instance of lti)

• 2: (num, den)

• 3: (zeros, poles, gain)

• 4: (A, B, C, D)

dtfloat

The discretization time step.

method{“gbt”, “bilinear”, “euler”, “backward_diff”, “zoh”}, optional

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 alpha=0)

• backward_diff: Backwards differencing (“gbt” with alpha=1.0)

• zoh: zero-order hold (default)

alphafloat within [0, 1], optional

The generalized bilinear transformation weighting parameter, which should only be specified with method=”gbt”, and is ignored otherwise

Returns
sysdtuple 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

Notes

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].

References

1

https://en.wikipedia.org/wiki/Discretization#Discretization_of_linear_state_space_models

2

http://techteach.no/publications/discretetime_signals_systems/discrete.pdf

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. (https://www.mypolyuweb.hk/~magzhang/Research/ZCC09_IJC.pdf)

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