Number of blocks in Generalized matrix or Generalized LTI model
N = nblocks(M)
returns the number of Control Design Blocks in the Generalized LTI model or Generalized matrix
N
= nblocks(M
)M
.

A Generalized LTI model ( 

The number of Control Design Blocks in
If 
Number of Control Design Blocks in a SecondOrder Filter Model
This example shows how to use nblocks
to examine two
different ways of parameterizing a model of a secondorder filter.
Create a tunable (parametric) model of the secondorder filter:
$$F\left(s\right)=\frac{{\omega}_{n}^{2}}{{s}^{2}+2\zeta {\omega}_{n}+{\omega}_{n}^{2}},$$
where the damping ζ and the natural frequency ω_{n} are tunable parameters.
wn = realp('wn',3); zeta = realp('zeta',0.8); F = tf(wn^2,[1 2*zeta*wn wn^2]);
F
is a genss
model with
two tunable Control Design Blocks, the realp
blocks
wn
and zeta
. The blocks
wn
and zeta
have initial
values of 3 and 0.8, respectively.
Examine the number of tunable blocks in the model using
nblocks
.
nblocks(F)
This command returns the result:
ans = 6
F
has two tunable parameters, but the parameter
wn
appears five times — twice in the
numerator and three times in the denominator.
Rewrite F
for fewer occurrences of
wn
.
The secondorder filter transfer function can be expressed as follows:
$$F\left(s\right)=\frac{1}{{\left(\frac{s}{{\omega}_{n}}\right)}^{2}+2\zeta \left(\frac{s}{{\omega}_{n}}\right)+1}.$$
Use this expression to create the tunable filter:
F = tf(1,[(1/wn)^2 2*zeta*(1/wn) 1])
Examine the number of tunable blocks in the new filter model.
nblocks(F)
This command returns the result:
ans = 4
In the new formulation, there are only three occurrences of the
tunable parameter wn
. Reducing the number of
occurrences of a block in a model can improve performance time of
calculations involving the model. However, the number of occurrences
does not affect the results of tuning the model or sampling the model
for parameter studies.