This is machine translation

Translated by Microsoft
Mouse over text to see original. Click the button below to return to the English verison of the page.

Creating Discrete-Time Models

This example shows how to create discrete-time linear models using the tf, zpk, ss, and frd commands.

Specifying Discrete-Time Models

Control System Toolbox™ lets you create both continuous-time and discrete-time models. The syntax for creating discrete-time models is similar to that for continuous-time models, except that you must also provide a sample time (sampling interval in seconds).

For example, to specify the discrete-time transfer function:

$$ H(z) = \frac{z - 1}{z^2 - 1.85 z + 0.9} $$

with sampling period Ts = 0.1 s, type:

num = [ 1  -1 ];
den = [ 1  -1.85  0.9 ];
H = tf(num,den,0.1)
H =
        z - 1
  z^2 - 1.85 z + 0.9
Sample time: 0.1 seconds
Discrete-time transfer function.

or equivalently:

z = tf('z',0.1);
H = (z - 1) / (z^2 - 1.85*z + 0.9);

Similarly, to specify the discrete-time state-space model:

$$ x[k+1] = 0.5 x[k] + u[k] $$

$$ y[k] = 0.2 x[k] . $$

with sampling period Ts = 0.1 s, type:

sys = ss(.5,1,.2,0,0.1);

Recognizing Discrete-Time Systems

There are several ways to determine if your LTI model is discrete:

  • The display shows a nonzero sample time value

  • sys.Ts or get(sys,'Ts') return a nonzero sample time value.

  • isdt(sys) returns true.

For example, for the transfer function H specified above,

ans =


ans =



You can also spot discrete-time systems by looking for the following traits:

  • Time response plots - Response curve has a staircase look owing to its sampled-data nature

  • Bode plots - There is a vertical bar marking the Nyquist frequency (pi divided by the sample time).

The following plots show these characteristic traits:


bode(H), grid

Was this topic helpful?