nblocks
Number of blocks in Generalized matrix or Generalized LTI model
Syntax
N = nblocks(M)
Description
returns the number of Control Design Blocks in the Generalized LTI model or Generalized matrix
N
= nblocks(M
)M
.
Input Arguments
|
A Generalized LTI model ( |
Output Arguments
|
The number of Control Design Blocks in
If |
Examples
Number of Control Design Blocks in a Second-Order Filter Model
This example shows how to use nblocks
to examine two
different ways of parameterizing a model of a second-order filter.
Create a tunable (parametric) model of the second-order filter:
where the damping ζ and the natural frequency ωn are tunable parameters.
wn = realp('wn',3); zeta = realp('zeta',0.8); F = tf(wn^2,[1 2*zeta*wn wn^2]);
F
is agenss
model with two tunable Control Design Blocks, therealp
blockswn
andzeta
. The blockswn
andzeta
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 parameterwn
appears five times — twice in the numerator and three times in the denominator.Rewrite
F
for fewer occurrences ofwn
.The second-order filter transfer function can be expressed as follows:
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.
Version History
Introduced in R2011a