Chapter 1
Introduction
1-8
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•
The controllability grammian is also
E
[
x
(
t
)
x
′
(
t
)] when the system
has been excited from time –
∞
by zero mean white
noise with
.
•
The observability grammian can be thought of as measuring the
information contained in the output concerning an initial state.
If
with
x
(0) =
x
0
then:
Systems that are easy to observe correspond to
Q
with large
eigenvalues, and thus large output energy (when unforced).
•
lyapunov(A,B*B')
produces
P
and
lyapunov(A',C'*C)
produces
Q
.
For a discrete-time
G
(
z
) =
D
+
C
(
zI
-
A
)
–1
B
with |
λ
i
(
A
)|<1,
P
and
Q
are:
P – APA
′
= BB
′
Q – A
′
QA = C
′
C
The first dot point above remains valid. Also,
•
and
with the sums being finite in case
A
is nilpotent (which is the case if
the transfer-function matrix has a finite impulse response).
•
[
I
–
A
⊗
A
] vec
P
= vec
(BB
′)
lyapunov( )
can be used to evaluate
P
and
Q
.
Hankel Singular Values
If
P
,
Q
are the controllability and observability grammians of a
transfer-function matrix (in continuous or discrete time), the
Hankel
Singular Values
are the quantities
λ
i
1/2
(
PQ
). Notice the following:
•
All eigenvalues of
PQ
are nonnegative, and so are the Hankel singular
values.
•
The Hankel singular values are independent of the realization used to
calculate them: when
A
,
B
,
C
,
D
are replaced by
TAT
–1
,
TB
,
CT
–1
and
D
,
then
P
and
Q
are replaced by
TPT
′
and (
T
–1
)
′
QT
–1
; then
PQ
is replaced
by
TPQT
–1
and the eigenvalues are unaltered.
•
The number of nonzero Hankel singular values is the order or
McMillan degree of the transfer-function matrix, or the state
dimension in a minimal realization.
x
·
Ax Bw
+
=
E w t
( )
w
′
s
( )
[
]
I
δ
t s
–
(
)
=
x
·
Ax
=
y
,
Cx
=
y
′
t
( )
y t
( )
dt
0
∞
∫
x
′
0
Qx
0
=
P
A
k
BB
′
A
′
k
k
0
=
∞
∑
=
Q
A
k
C
′
CA
′
k
k
0
=
∞
∑
=