Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng ....
Transcript of Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng ....
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Model Reduction for Dynamical Systems
-Lecture 3- Peter Benner and Lihong Feng
Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017
Max Planck Institute for Dynamics of Complex Technical Systems Computational Methods in Systems and Control Theory
Magdeburg, Germany [email protected] [email protected]
http://www.mpi-magdeburg.mpg.de/csc/teaching/17ss/mor
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Outline
• Mathematical basics II Systems and control theory
• Controllability measures • Observability measures
• Infinite Gramians
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Motivation
Balanced truncation: first balancing, then truncate.
Given a LTI system:
)()(
)()(/)(
txLty
tButAxdttdxT=
+=
For convenience of discussion, we denote the system as a block form:
TL
BA
=
TTT
LLBAABAA
LBA
21
22221
11211
~~~~~~~~
~~~
TL
BA
1
111~
~~
Balancing T
truncate reduced model The unimportant
part is truncated
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What’s the unimportant part?
The states which are difficult to control and difficult to observe correspond the unimportant part.
In system theory, the unknown vector x is called the state (vector) of the system. Actually, the entries in x depict the system variables, such as branch currents, node voltages in the interconnect model, and therefore describe the state of the system.
Motivation
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Outline
• Controllability measures • Observability measures
• Infinite Gramians
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Controllability measure
Reachability
Definition: Given a system , a state x is reachable from the zero state if there exist an input function of finite energy such that x can be obtain from the zero state and within a finite period of time .
TL
BA
)(tu∞<t
C
A
B
x (0)=0 1 y x )(tu
∞<t
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reachX
Controllability measure
Denote the subspace spanned by the reachable states, then
XX reach =
XX reach ⊆
The system is reachable : every state in the state space is reachable.
is the whole state space, e.g.
:)( nCRtxX →= +
X
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Controllability measure
Example 1
x +
_ +
_
Ω1
Ω2
Ω4
Ω8
V
x denotes the voltage drop along the capacitor, and is the state of the system. In this circuit, x=0 at any time.
Conclusion: In this circuit, 0 state is a reachable state, but any nonzero state is a unreachable state! Therefore the whole system is unreachable.
Picture referred to [Chi-Tsong Chen, Linear system Theory and Design, 3rd edition, New York Oxford, Oxford University Press, 1999]
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Wheatstone bridge
A Wheatstone bridge is a measuring instrument invented by Samuel Hunter Christie in 1833 and improved and popularized by Sir Charles Wheatstone in 1843. (http://en.wikipedia.org/wiki/Wheatstone_bridge)
Example 1 is actually the Wheatstone bridge.
+
_
1R
2R
3R
xR
VGV
is adjustable, it is adjusted till becomes zero. It means there is no voltage drop through . Therefore, we have
3R
GVGV
31
2
RR
RR x=
xR can be easily adjusted by the above equation.
Controllability measure
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Controllability measure
Example 2
+
_
F1
Ω1 Ω1
V
F1)(1 tx +
_ _
+ )(2 tx
)()(
)()(/)(
txLty
tButAxdttdxT=
+=
=
)()(
)(2
1
txtx
tx
voltage drops through the two capacitors.
Those states with are reachable, but those states with are not reachable. Because whatever the input is, the voltage drops through the two capacitors are always identical. Therefore the whole system is unreachable.
)(tx )()( 21 txtx =)()( 21 txtx ≠
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Controllability measure
Reachability matrix of the system:
],,[),( 12 BABAABBBAR n−=
By the Cayley-Halmilton theorem, the rank of the reachability matrix and the span of its columns are determined (at most) by the first n terms (not the first n columns), i.e. Thus for computational purpose the following (finite) reachability matrix is of importance:
.1,,2,1, −= ntBAt
],,[),( 12 BABAABBBAR nn
−=
Sometimes is directly defined as the reachability matrix. ),( BARn
• Why it is called reachability matrix? • Any connection between and reachability? ),( BARn
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Controllability measure
Notice the analytical solution of system state equation is BuAxdtdx +=/
∫ ≥+= −tt
tAAt ttdBuexetxux0
,,)(),,( 0)(
00 τττ
The reachability of a state x of the system is tested by the zero initial state, , we look at the above analytical solution with , 00 =x
∫ −=t tA dBuetux0
)(),0,( )( τττ
Notice:
+++++= kk
nAt A
ktAtAtIe
!!2!12
2
00 =x
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,)()()()(
)(!2
)()()()(
)()!2
)()(()(),0,(
22
10
0 0
22
0
22
0
)(
0
+++++=
−+−+=
+−
+−+==
∫ ∫∫
∫∫ −
tBAtBAtABtB
dutBAdutABduB
duBAtABtBdBuetux
kk
t tt
tt tA
αααα
ττττττττ
τττττττ
which means a reachable state x is the linear combination of the terms:
,,,,, 2 BABAABB k
Therefore is defined as the reachability Matrix.
),,(),( 12 BABAABBAR n−=
Controllability measure
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Controllability measure
Actually there is a Theorem (Theorem 4.5 in Chapter 4 in [Antoulas05]):
Theorem 1 If is the subspace spanned by the reachable states, then reachXcolumns. by the spanned subspace :),( im BARX reach =
The theorem tells us the subspace spanned by all reachable states is exactly the subspace spanned by the columns of the reachability matrix . ),( BAR
The finite reachability gramian at time is defined as : ∞<t
∞<<= ∫ tfordeBBetPt ATA T
0,)(0
τττ
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Controllability measure
Connection between reachability matrix and reachability gramians
Proposition 1 The finite reachability gramians have the following properties: (a) and (b) their columns span the reachability subspace, i.e.,
,0)()( ≥= tPtP T
).,( im)( im BARtP =
Proof An easier way is to prove nn CBARBARCtPtP =⊕=⊕ ⊕⊕ ),( im),( imand)( im)( im
where),,( im)( im BARtP ⊕⊕ =
⊕∈∀ Px im we have
,0||||)(0
2 == ∫t ATT dxeBxtPx
T
ττ
0,0 ≥=⇔ tallforxeB tAT T
We first prove ),( im)( im BARxtPx ⊕⊕ ∈⇒∈∀
( Proposition 4.8 in [Antoulas 05] )
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Controllability measure
+++++= kTk
TTn
tA AktAtAtIe
T)(
!)(
!2!12
2
.0 allfor ,0)(0 Therefore, 1 >=⇔= − ixABxeB iTTtAT T
BAx i 1−⊥
),( im BARx ⊥
),( im BARx ⊕∈
We have proved: ),( im)( im BARxtPx ⊕⊕ ∈⇒∈∀
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Controllability measure ⊕⊕ ∈⇒∈∀ PxBARx im),( imNext we prove:
),( im BARx ⊕∈ ),( im BARx ⊥ 0,1 >⊥ − iallforBAx i
.0,0)( 1 >=− iallforxAB iTT
0,0 ≥= tallforxeB tAT T
0=xeBBe tATAt T
,0)(0
== ∫t ATA xdeBBextP
Tτττ)(Pnullx∈
)( im Px ⊥
⊕∈ Px im
P symmetric
why?
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Controllability measure
)( null Px∈∀ 0=Px
==
Tn
T
T
Tn
T
T
p
pp
Pand
xp
xpxp
2
1
2
1
0
)( null Ppi ⊥ )( null)( im PPT ⊥
P symmetric
,,span of columnsspan )( im 1 nTT ppPP ==
)( im)( im TPP =
)( null)( im PP ⊥
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The states using large minimal energy are difficult to reach and will be truncated during MOR based on balanced truncation.
The relation provides a way to derive the minimal energy which is needed to reach a state x.
),( im)( im BARtP =
Controllability measure
Therefore, the minimal energy for reaching a reachable state x is a key concept for model order reduction based on balanced truncation.
Next, we will derive the minimal energy for reaching a state x.
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Controllability measure
From the analytical solution, if a state x is reached at time , then with finite energy, such that
τττ dBuexT TA∫ −=0
)( )(
T
How much must the input u(t) be?
We have proved if x is reachable, then , i.e. ))(( im tPx∈
τ
τξξξ
τ
ττ
duBe
deBBedteBBexTPxT TA
TATT TAT tATAt TT
∫
∫∫−
−−
=
−==⇒=
0
)(
)(
0
)(
0)()(
ξτ τ )()( −−= TAT T
eBuand
,,Tξ∃
)(tu∃
This means x can be reached at time with input T u
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Controllability measure
C
A
B
x (0)=0 1 y x )(tu
∞<t
The input u(t) is the excitation of the system, its energy is the energy required to reach the state x .
Energy of a function is defined as: dttutuuT
)()(||||0
2 ∫ ∗=
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Controllability measure
Actually the energy of is the minimal energy to reach the state x at the given time period . (Proposition 4.10 in [Antoulas 05])
u
ξξξξ )()()(|||| )()(
00
2 tPdteBBedttutuu TttATttAt Tt T T
=== −−∫∫
We see from above analysis, if x is reachable at time , x can be represented as:
ττ duBext tA∫ −=0
)(
t
22 ||)(||||)(|| tutu > Any other input can also reach x. However if , it cannot reach x at time , but needs longer time.
t
t
22 ||)(||||)(|| tutu <
)( )( ξτ−−= tAT T
eBu
Energy of : u
x
relation to x?
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Controllability measure
A system is reachable means every state x in the whole state space is reachable.
),( im),( im BARBARX nreach ==
nBARn =)),((rank
From theorem 1:
From Proposition 1: ),( im)( im BARtP =
Therefore the system is reachable
Therefore the system is reachable 0 ,))((rank >∀= tntP
Therefore, is nonsingular for any t, if the system is reachable. )(tP
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Controllability measure
Energy of (notice ) : ξτ )( −= tAT TeBu
xtPxxtPtPxtPtPu TTT )())()(())(()(|||| 1112 −−− === ξξ
ξ)(tPx =
Controllability measure!
xtPxu T )(|||| 12 −=
Only for reachable systems.
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Remark 1: Reachability is a generic property for LTI systems with the form: This means, intuitively, that almost every LTI system with the form above is reachable. If there are any unreachable systems, they are very rare. The unreachable LTI systems like examples 1,2 are rare.
Controllability measure
BuAxdtdx +=/
Remark 2: The reachability of the system can be more easily checked by the criteria:
The system is reachable nBARrank n =)),((
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Controllability measure
A concept which is closely related to reachability is that of controllability. Here, instead of driving the zero state to a desired state, a given non-zero state is steered to the zero state. More precisely we have:
Definition of controllability: Given a LTI system as above, a non-zero state x is controllable if there exist an input u(t) with finite energy such that the state of the system goes to zero from x within a finite time: . ∞<t
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Controllability measure
Theorem 2 For time continuous systems . (Theorem 4.16 in [Antoulas 05])
contrreach XX =
Similarly, is the subspace spanned by the controllable states.
It has been proved that for time continuous LTI systems (as discussed in this lecture), the concepts of reachability and controllability are equivalent.
contrX
The system is controllable
From the property of reachable system, we have
nBARn =)),((rank
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Controllability measure
Spring Constant: 1
Damping Coefficient: 1
Damping Coefficient: 2
2u(t)
1x
dashpot dashpot
2xExample: Platform system
The system is described by the following linear time invariant (LTI) system:
)(15.0
)(10
05.0/)( tutxdttdx
+
−
−=
A B
Spring Constant: 1
002
22
11
=−−=−−
xxuxxu
makxvF =−−ηassume mass of the platform is zero, then from Newton’s law:
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Controllability measure
Is the platform system controllable?
nBARrank n =)),((The system is controllable
],,[),( ABBBARn =
=
15.0
B
−
−=
−
−=
125.0
15.0
1005.0
AB
ABB, are linearly independent!
nBARrank n == 2)),((
Therefore, the platform system is controllable.
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Controllability measure
Associated with controllability, there is the concept of observability.
Controllability: input u(t) state x(t).
Possibility of steering the state using the input.
Observability: output y(t) state x(t).
Possibility of estimating the state from the output.
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Outline
• Observability measures
• Infinite Gramians
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Observability measure
Observability is a measure for how well internal states of a system can be estimated by knowledge of its external outputs.
Definition of Observability: Given any input u(t) , a state x of the system is observable, if starting with the state x (x(0)=x), and after a finite period of time , x can be uniquely determined by the output . ∞<t
C
A
B u(t)
x(0)=x
1
)(ty
∞<t
)(ty
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Observability measure
Observability matrix? Observability Gramian? Output energy?
=
2),(ALAL
L
ALO T
T
T
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Observability measure Derivation of Observability matrix
τττ dBuexetxt tAtA ∫ −+=0
)(0 )()(~
From the analytical solution of , we see that after time : BuAxdtdx +=/ ∞<t
The system starting with x(0)=x, therefore
τττ dBuexetxt tAtA ∫ −+=0
)( )()(~
And the output corresponding to is: )(~ tx
∫∫
∫
−
−
−
+==
+=
+==
t AtAT
t AtATtAT
t tATtATT
dBuexxandxeL
dBueeLxeL
dBueLxeLtxLty
0
0
0
)(
)(
)(
)()(~)(
ττ
ττ
ττ
τ
τ
τ
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If x is observable, then for any u(t), x can be uniquely determined by the corresponding y :
∫−+== t AtAT dBuexxandxeLty 0 )()( τττ
Since x can be uniquely determined by , it is sufficient to prove that can be uniquely determined by .
Let us see under what condition can be uniquely determined by ? x
Observability measure Derivation of Observability matrix
x x)(ty
)(ty
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xeLty tAT=)(
Differentiate the above equation on both sides and get the derivatives at t=0:
xLy T=)0(
xALy T=)0('
xALy T 2'' )0( =
xALy kTk =)0()(
=
)0(
)0()0(
)(
'
kkT
T
T
y
yy
x
AL
ALL
(#) has a unique solution if is square and has full rank n.
(#)
x
kT
T
T
AL
ALL
Observability measure Derivation of Observability matrix
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=
kT
T
T
k
AL
ALL
Q
Denote:
yQx k1−=
=
)0(
)0()0(
)(
'
ky
yy
y
can be uniquely determined, with k being at most n-1. x
if m>1, then k<n-1, if m=1, k=n-1. nmT RL ×∈
Observability measure Derivation of Observability matrix
![Page 38: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/38.jpg)
Observability matrix:
=
2),(ALAL
L
ALO T
T
T
Therefore:
=
−1
),(
nT
T
T
n
AL
ALL
ALO
From above analysis, actually the finite Observability matrix is enough to determine observability:
Therefore we define
The system is observable nALOrank n =)),((
Observability measure Derivation of Observability matrix
![Page 39: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/39.jpg)
The output energy associated with the initial state x is:
dtxeLLexdttytyty AtTtAt Tt T T
∫∫ ==00
2 )()(||)(||
xtQx
xdteLLexT
t AtTtAT T
)(0
=
= ∫
1. Energy of observation produced by an observable state x. 2. Observability measure!
Observability measure Output energy
Finite Observability Gramian at time is defined as: ∞<t
∞<<= ∫ tdeLLetQt ATAT
0,)(0
τττ
![Page 40: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/40.jpg)
Observability measure Observability Gramian
Recall the minimal energy to reach a state x at time is txtPxu T )(|||| 12 −=
Notice both energies are related to time.
xtQxty T )(||)(|| 2=xtPxu T )(|||| 12 −=
Finite (reachability) controllability Gramian and observability Gramian will be used to derive the infinite Gramians which 1. Make the two measures computable. 2. will be directly used for truncation in MOR.
∞<<= ∫ tdeBBetPt ATA T
0,)(0
τττ ∞<<= ∫ tdeLLetQt ATA
T
0,)(0
τττ
![Page 41: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/41.jpg)
Outline
• Infinite Gramians
![Page 42: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/42.jpg)
Under which condition, and are bounded when time goes to infinity: ?
)(tQ )(tP∞→t
Roughly speaking, and can be bounded when , if
∞<<= ∫ tdeLLetQt ATA
T
0,)(0
τττ
∞<<= ∫ tdeBBetPt ATA T
0,)(0
τττ
Ate∞→t
Infinite Gramians make the two measures computable
∞→tis bounded when .
)(tQ )(tP
![Page 43: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/43.jpg)
is bounded if the real parts of all the eigenvalues of A are negative. Ate
Why? Let be the eigen-decomposition of A, SSA Λ=−1
SeeSSeSSeSee tttttStSAt imreimre ΛΛ−Λ+Λ−Λ−Λ ====− 1111
=Λ
ren
re
re
re
λ
λλ
2
1
=Λ
imn
im
im
im
j
jj
λ
λλ
2
1
nij imi
reii ,2,1, =+= λλλ are eigenvalues of A.
Infinite Gramians make the two measures computable
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SeeSSeSee imre tttStSAt ΛΛ−Λ−Λ ===− 111
=Λ
ren
re
re
re
t
t
t
t
e
ee
e
λ
λ
λ
2
1
=Λ
imn
im
im
im
tj
tj
tj
t
e
ee
e
λ
λ
λ
2
1
∞→t
0
00
0<reiλ
∞→t
)sin()cos( imi
imi
tj jteimi λλλ +=
bounded
Infinite Gramians make the two measures computable
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0111→=== ΛΛ−Λ−Λ−
SeeSSeSee imre ttSSAt Therefore,
if the real parts of all the eigenvalues of A are negative.
dteBBedeBBetPP tATAtt ATAtt
TT
∫∫∞
∞→∞→===
00lim)(lim τττ
Therefore the follow limits exists if all the eigenvalues of A are negative, i.e. if the system is stable:
dteLLedeLLetQQ AtTtAt ATAtt
TT
∫∫∞
∞→∞→===
00lim)(lim τττ
where P and Q are the infinite Gramians (only for stable systems).
Infinite Gramians make the two measures computable
![Page 46: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/46.jpg)
The infinite Gramians:
dteBBedeBBetPP tATAtt ATA
tt
TT
∫∫∞
∞→∞→===
00lim)(lim τττ
dteLLedeLLetQQ AtTtAt ATAtt
TT
∫∫∞
∞→∞→===
00lim)(lim τττ
From the property of integral, we have
ttPP ∀≥ ),( ttQQ ∀≥ ),(
Infinite Gramians make the two measures computable
In the meaning of inner product: ),)((),()( xxtPxPxtPP ≥⇔≥
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For stable systems, lower bound of the minimal energy necessary for reaching a reachable state x is:
xPxxtPxu TT 112 )(|||| −− ≥=
The minimal energy necessary for reaching a reachable state x at time t is:
xtPxu T )(|||| 12 −=
because ttPP ∀≥ ),(
For stable systems, the upper bound of the energy produced by the observable state x is:
xQxxtQxty TT ≤= )(||)(|| 2 because ttQQ ∀≥ ),(
Only suitable for stable systems!
Infinite Gramians make the two measures computable
Computable measures!
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For stable systems, the minimal energy necessary for reaching any state is:
xPxu T 12||||min −=
For stable systems, the maximal energy produced by any state x is:
xQxty T=2||)(||max
Infinite Gramians make the two measures computable
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Because the MOR method we will introduce uses P and Q to derive the reduced-order model, and therefore is only suitable for stable systems.
xPxu T 12||||min −= xQxty T=2||)(||max
The eigenspaces of P and Q make the two measurements practically computable!
Infinite Gramians make the two measures computable
![Page 50: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/50.jpg)
The states which are difficult to reach are included in the subspace spanned by those eigenvectors of P that corresponds to small eigenvalues.
The states which are difficult to observe are included in the subspace spanned by those eigenvectors of Q that corresponds to small eigenvalues.
why and how?
Eigenspaces of P and Q make the two measures parctically computable
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nξξξ ,,, 21 Denote as the n eigenvectors of P, the corresponding eigenvalues are . (P is symmetric positive definite, it has positive eigenvalues.)
nC
The state x can therefore be represented by : nξξξ ,,, 21
nnx ξαξαξα +++= 2211
xPxu T 12||||min −=
If a matrix is nonsingular, then its inverse has the same eigenvectors, but the eigenvalues are the reciprocals:
ξλξξλξλξξ 111 / −−− =⇒=⇒= PPPPP
are linearly independent, therefore they constitute a basis of the whole space .
nξξξ ,,, 21
nλλλ ≥≥≥ 21
Eigenspaces of P and Q make the two measures practically computable
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xPxu T 12||||min −=
nnx ξαξαξα +++= 2211
nn
nxP ξλ
αξλ
αξλ
α 1112
221
11
1 +++=−
nTn
nn
TTT xPx ξξλ
αξξλ
αξξλ
α 111 222
2
2211
1
21
1 +++=−
nnuλ
αλ
αλ
α 111||||min 2
2
22
1
21 +++=
indicates the minimal energy needed to reach the state x, therefore the larger is, the more difficult the state x to reach.
2||||min u2||||min u
Therefore is orthogonal. P is symmetric, ],,[~1 nQ ξξ =
Eigenspaces of P and Q make the two measures practically computable
![Page 53: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/53.jpg)
nn λλλ
λλλ 111
2121 ≤≤≤⇒≥≥≥
2||||min u
This means if x is difficult to reach ( is large), x should have large components in the subspace spanned by the eigenvectors corresponding to the small eigenvalues of P. Or x should almost locates in the subspace spanned by the eigenvectors corresponding to the small eigenvalues.
nnx ξαξαξα +++= 2211
is larger if and
2|||| u
nkk λλλλλ ≥≥≥>>≥≥ + 121
nkk ααααα ,,,,, 121 +<<
nnuλ
αλ
αλ
α 111||||min 2
2
22
1
21 +++=
Eigenspaces of P and Q make the two measures practically computable
than if
nkk λλλλλ ≥≥≥>>≥≥ + 121 and
nkk ααααα ,,,,, 121 +>>
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Similarly, if x is difficult to observe ( is small) x should have large components in the subspace spanned by the eigenvectors corresponding to the small eigenvalues of Q. Or x should almost locates in the subspace spanned by the eigenvectors corresponding to the small eigenvalues.
xQxty T=2||)(||
kξ2ξ
1ξ
1+kξ2+kξ nξ
x
niP iii ,2,1, == ξλξ
niQ iii ,2,1,~~~== ξλξ
nkk λλλλλ ≥≥≥>>≥≥ + 121
nkk λλλλλ ~~~~~121 ≥≥≥>>≥≥ +
Eigenspaces of P and Q make the two measures practically computable
![Page 55: Model Reduction for Dynamical Systems -Lecture 3- · -Lecture 3- Peter Benner and Lihong Feng . Otto-von-Guericke Universität Magdeburg Faculty of Mathematics Summer term 2017 .](https://reader034.fdocuments.in/reader034/viewer/2022050211/5f5d7eb95870dd6497786625/html5/thumbnails/55.jpg)
[1] A.C. Antoulas, “Approximation of large-scale Systems”, SIAM Book Series: Advances in Design and Control, 2005. [2] Chi-Tsong Chen, Linear system Theory and Design, 3rd edition, New York Oxford, Oxford University Press, 1999.
References