iopzplot
Plot pole-zero map for I/O pairs and return plot handle
Syntax
h = iopzplot(sys)
iopzplot(sys1,sys2,...)
iopzplot(AX,...)
iopzplot(..., plotoptions)
Description
h = iopzplot(sys) computes and plots the
poles and zeros of each input/output pair of the dynamic system model sys.
The poles are plotted as x's and the zeros are plotted as o's. It
also returns the plot handle h. You can use this handle to customize
the plot with the getoptions and setoptions commands.
Type
help pzoptions
for a list of available plot options. For more information on the ways to change properties of your plots, see Ways to Customize Plots.
iopzplot(sys1,sys2,...) shows the poles
and zeros of multiple dynamic system models sys1,sys2,... on
a single plot. You can specify distinctive colors for each model,
as in
iopzplot(sys1,'r',sys2,'y',sys3,'g')
iopzplot(AX,...) plots into the axes with
handle AX.
iopzplot(..., plotoptions) plots the poles
and zeros with the options specified in plotoptions.
Type
help pzoptions
for more detail.
The function sgrid or zgrid can be used
to plot lines of constant damping ratio and natural frequency in the
s or z plane.
For arrays sys of LTI models, iopzplot plots
the poles and zeros of each model in the array on the same diagram.
Examples
Change I/O Grouping on Pole/Zero Map
Create a pole/zero map of a two-input, two-output dynamic system.
sys = rss(3,2,2); h = iopzplot(sys);
By default, the plot displays the poles and zeros of each I/O pair on its own axis. Use the plot handle to view all I/Os on a single axis.
setoptions(h,'IOGrouping','all')
Use Pole-Zero Map to Examine Identified Model
View the poles and zeros of a sixth-order state-space model estimated from input-output data. Use the plot handle to display the confidence intervals of the identified model's pole and zero locations.
load iddata1 sys = ssest(z1,6,ssestOptions('focus','simulation')); h = iopzplot(sys); showConfidence(h)
There is at least one pair of complex-conjugate poles whose locations overlap with those of a complex zero, within the 1-σ confidence region. This suggests their redundancy. Hence, a lower (4th) order model might be more robust for the given data.
sys2 = ssest(z1,4,ssestOptions('focus','simulation')); h = iopzplot(sys,sys2); showConfidence(h) legend('6th-order','4th-order') axis([-20, 10 -30 30])
The fourth-order model sys2 shows less variability in the pole-zero locations.