The ucomplex
element
is the Control
Design Block that represents an uncertain complex number.
The value of an uncertain complex number lies in a disc, centered
at NominalValue
, with radius specified by the Radius
property
of the ucomplex
element. The size of the disc can
also be specified by Percentage
, which means the
radius is derived from the absolute value of the NominalValue
.
The properties of ucomplex
objects
are
Properties  Meaning  Class 

 Internal Name 

 Nominal value of element 




 Radius of disk  
 Additive variation (percent of  



The simplest construction requires only a name and nominal value.
Displaying the properties shows that the default Mode
is Radius
,
and the default radius is 1.
a = ucomplex('a',2j)
a = Uncertain complex parameter "a" with nominal value 21i and radius 1.
get(a)
Name: 'a' NominalValue: 2.0000  1.0000i Mode: 'Radius' Radius: 1 Percentage: 44.7214 AutoSimplify: 'basic'
Sample the uncertain complex parameter at 400 values, and plot in the complex plane. Clearly, the samples appear to be from a disc of radius 1, centered in the complex plane at the value 2j.
asample = usample(a,400);
plot(asample(:),'o');
xlim([0.5 4.5]);
ylim([3 1]);
The uncertain complex matrix class, ucomplexm
,
represents the set of matrices given by the formula
N + W_{L}ΔW_{R}
where N, W_{L},
and W_{R} are known matrices,
and Δ is any complex matrix with $$\dot{\overline{\sigma}}\left(\Delta \right)\le 1$$.
All properties of a ucomplexm
are
can be accessed with get
and set
.
The properties are
Properties  Meaning  Class 

 Internal Name 

 Nominal value of element 

 Left weight 

 Right weight 




Create a 4by3 uncertain complex matrix (ucomplexm
), and view its properties. The simplest construction requires only a name and nominal value.
m = ucomplexm('m',[1 2 3; 4 5 6; 7 8 9; 10 11 12])
m = Uncertain complex matrix "m" with 4 rows and 3 columns.
get(m)
Name: 'm' NominalValue: [4×3 double] WL: [4×4 double] WR: [3×3 double] AutoSimplify: 'basic'
The nominal value is the matrix you supply to ucomplexm
.
mnom = m.NominalValue
mnom = 1 2 3 4 5 6 7 8 9 10 11 12
By default, the weighting matrices are the identity. For example, examine the left weighting.
m.WL
ans = 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1
Sample the uncertain matrix, and compare to the nominal value. Note the elementbyelement sizes of the difference are roughly equal, indicative of the identity weighting matrices.
msamp = usample(m); diff = abs(msampmnom)
diff = 0.3309 0.0917 0.2881 0.2421 0.3449 0.3917 0.2855 0.2186 0.2915 0.3260 0.2753 0.3816
Change the left and right weighting matrices, making the uncertainty larger as you move down the rows, and across the columns.
m.WL = diag([0.2 0.4 0.8 1.6]); m.WR = diag([0.1 1 4]);
Sample the uncertain matrix again, and compare to the nominal value. Note the elementbyelement sizes of the difference, and the general trend that the smallest differences are near the (1,1) element, and the largest differences are near the (4,3) element, consistent with the trend in the diagonal weighting matrices.
msamp = usample(m); diff = abs(msampmnom)
diff = 0.0048 0.0526 0.2735 0.0154 0.1012 0.4898 0.0288 0.3334 0.8555 0.0201 0.4632 1.3783