National Instruments 370755B-01 User Manual
TM
MATRIXx
XmathTM Model Reduction Module
Xmath Model Reduction Module
April 2004 Edition
Part Number 370755B-01
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Conventions
The following conventions are used in this manual:
[ ] Square brackets enclose optional items—for example, [
brackets also cite bibliographic references.
» The » symbol leads you through nested menu items and dialog box options
to a final action. The sequence File»Page Setup»Options directs you to
pull down the File menu, select the Page Setup item, and select Options
from the last dialog box.
This icon denotes a note, which alerts you to important information.
bold Bold text denotes items that you must select or click in the software, such
as menu items and dialog box options. Bold text also denotes parameter
names.
italic Italic text denotes variables, emphasis, a cross reference, or an introduction
to a key concept. This font also denotes text that is a placeholder for a word
or value that you must supply.
monospace Text in this font denotes text or characters that you should enter from the
keyboard, sections of code, programming examples, and syntax examples.
This font is also used for the proper names of disk drives, paths, directories,
programs, subprograms, subroutines, device names, functions, operations,
variables, filenames, and extensions.
monospace bold Bold text in this font denotes the messages and responses that the computer
automatically prints to the screen. This font also emphasizes lines of code
that are different from the other examples.
response]. Square
monospace italic Italic text in this font denotes text that is a placeholder for a word or value
that you must supply.
Contents
Chapter 1
Introduction
Using This Manual.........................................................................................................1-1
Document Organization...................................................................................1-1
Bibliographic References ................................................................................1-2
Commonly Used Nomenclature ......................................................................1-2
Conventions.....................................................................................................1-2
Related Publications ........................................................................................1-3
MATRIXx Help...............................................................................................1-3
Overview........................................................................................................................1-3
Functions .........................................................................................................1-4
Nomenclature .................................................................................................1-6
Commonly Used Concepts ............................................................................................1-7
Controllability and Observability Grammians ................................................1-7
Hankel Singular Values...................................................................................1-8
Internally Balanced Realizations.....................................................................1-10
Singular Perturbation.......................................................................................1-11
Spectral Factorization......................................................................................1-13
Low Order Controller Design Through Order Reduction .............................................1-15
Chapter 2
Additive Error Reduction
Introduction....................................................................................................................2-1
Truncation of Balanced Realizations.............................................................................2-2
Reduction Through Balanced Realization Truncation...................................................2-4
Singular Perturbation of Balanced Realization..............................................................2-5
Hankel Norm Approximation ........................................................................................2-6
balmoore( ).....................................................................................................................2-8
Related Functions ............................................................................................2-11
truncate( ).......................................................................................................................2-11
Related Functions ............................................................................................2-11
redschur( )......................................................................................................................2-12
Algorithm ........................................................................................................2-12
Related Functions ............................................................................................2-14
ophank( ) ........................................................................................................................2-14
Restriction........................................................................................................2-14
Algorithm ........................................................................................................2-15
Behaviors.........................................................................................................2-15
© National Instruments Corporation v Xmath Model Reduction Module
Contents
Onepass Algorithm ......................................................................................... 2-18
Multipass Algorithm ...................................................................................... 2-20
Discrete-Time Systems ................................................................................... 2-21
Impulse Response Error.................................................................................. 2-22
Unstable System Approximation .................................................................... 2-23
Related Functions............................................................................................ 2-23
Chapter 3
Multiplicative Error Reduction
Selecting Multiplicative Error Reduction......................................................................3-1
Multiplicative Robustness Result.................................................................... 3-2
bst( )............................................................................................................................... 3-3
Restrictions...................................................................................................... 3-4
Algorithm ........................................................................................................ 3-4
Algorithm with the Keywords right and left................................................... 3-5
Securing Zero Error at DC.............................................................................. 3-8
Hankel Singular Values of Phase Matrix of G
Further Error Bounds ...................................................................................... 3-9
Reduction of Minimum Phase, Unstable G .................................................... 3-9
Imaginary Axis Zeros (Including Zeros at ∞)................................................. 3-10
Related Functions............................................................................................ 3-14
mulhank( ) ..................................................................................................................... 3-14
Restrictions...................................................................................................... 3-14
Algorithm ........................................................................................................ 3-14
right and left .................................................................................................... 3-15
Consequences of Step 5 and Justification of Step 6........................................ 3-18
Error Bounds................................................................................................... 3-20
Imaginary Axis Zeros (Including Zeros at ∞)................................................. 3-21
Related Functions............................................................................................ 3-24
............................................... 3-9
r
Chapter 4
Frequency-Weighted Error Reduction
Introduction ................................................................................................................... 4-1
Controller Reduction....................................................................................... 4-2
Controller Robustness Result.......................................................................... 4-2
Fractional Representations.............................................................................. 4-5
wtbalance( ) ................................................................................................................... 4-10
Algorithm ........................................................................................................ 4-12
Related Functions............................................................................................ 4-15
fracred( ) ........................................................................................................................ 4-15
Restrictions...................................................................................................... 4-15
Defining and Reducing a Controller............................................................... 4-16
Xmath Model Reduction Module vi ni.com
Chapter 5
Utilities
hankelsv( )......................................................................................................................5-1
stable( ) ..........................................................................................................................5-2
compare( ) ......................................................................................................................5-4
Chapter 6
Tutorial
Plant and Full-Order Controller.....................................................................................6-1
Controller Reduction......................................................................................................6-5
Contents
Algorithm ........................................................................................................4-18
Additional Background ...................................................................................4-20
Related Functions ............................................................................................4-21
Related Functions ............................................................................................5-2
Algorithm ........................................................................................................5-2
Related Functions ............................................................................................5-4
ophank( )..........................................................................................................6-9
wtbalance.........................................................................................................6-12
fracred..............................................................................................................6-20
Appendix A
Bibliography
Appendix B
Technical Support and Professional Services
Index
© National Instruments Corporation vii Xmath Model Reduction Module
Introduction
This chapter starts with an outline of the manual and some useful notes. It
also provides an overview of the Model Reduction Module, describes the
functions in this module, and introduces nomenclature and concepts used
throughout this manual.
Using This Manual
This manual describes the Model Reduction Module (MRM), which
provides a collection of tools for reducing the order of systems.
Readers who are not familiar with Parameter Dependent Matrices (PDMs)
should consult the Xmath User Guide before using MRM functions and
tools. Although several MRM functions accept both PDMs and matrices as
input parameters, PDMs are preferable because they can include additional
information that is useful for simulation, plotting, and signal labeling.
Document Organization
This manual includes the following chapters:
• Chapter 1, Introduction, starts with an outline of the manual and some
useful notes. It also provides an overview of the Model Reduction
Module, describes the functions in this module, and introduces
nomenclature and concepts used throughout this manual.
• Chapter 2, Additive Error Reduction, describes additive error
reduction including discussions of truncation of, reduction by,
and perturbation of balanced realizations.
• Chapter 3, Multiplicative Error Reduction, describes multiplicative
error reduction presenting considerations for using multiplicative
rather than additive error reduction.
• Chapter 4, Frequency-Weighted Error Reduction, describes
frequency-weighted error reduction problems, including controller
reduction and fractional representations.
1
© National Instruments Corporation 1-1 Xmath Model Reduction Module
Chapter 1 Introduction
• Chapter 5, Utilities, describes three utility functions: hankelsv( ),
stable( ), and compare( ).
• Chapter 6, Tutorial, illustrates a number of the MRM functions and
their underlying ideas.
Bibliographic References
Throughout this document, bibliographic references are cited with
bracketed entries. For example, a reference to [VODM1] corresponds
to a paper published by Van Overschee and De Moor. For a table of
bibliographic references, refer to Appendix A, Bibliography.
Commonly Used Nomenclature
This manual uses the following general nomenclature:
• Matrix variables are generally denoted with capital letters; vectors are
represented in lowercase.
• G(s) is used to denote a transfer function of a system where s is the
Laplace variable. G(q) is used when both continuous and discrete
systems are allowed.
• H(s) is used to denote the frequency response, over some range of
frequencies of a system where s is the Laplace variable. H(q) is used
to indicate that the system can be continuous or discrete.
• A single apostrophe following a matrix variable, for example, x’,
denotes the transpose of that variable. An asterisk following a matrix
variable, for example, A*, indicates the complex conjugate, or
Hermitian, transpose of that variable.
Conventions
This publication makes use of the following types of conventions: font,
format, symbol, mouse, and note. These conventions are detailed in
Chapter 2, MATRIXx Publications, Online Help, and Customer Support,
of the MATRIXx Getting Started Guide.
Xmath Model Reduction Module 1-2 ni.com
Related Publications
For a complete list of MATRIXx publications, refer to Chapter 2,
MATRIXx Publications, Online Help, and Customer Support, of the
MATRIXx Getting Started Guide. The following documents are particularly
useful for topics covered in this manual:
• MATRIXx Getting Started Guide
• Xmath User Guide
• Control Design Module
• Interactive Control Design Module
• Interactive System Identification Module, Part 1
• Interactive System Identification Module, Part 2
• Model Reduction Module
• Optimization Module
• Robust Control Module
•X
MATRIXx Help
Model Reduction Module function reference information is available in
the MATRIXx Help. The MATRIXx Help includes all Model Reduction
functions. Each topic explains a function’s inputs, outputs, and keywords
in detail. Refer to Chapter 2, MATRIXx Publications, Online Help, and
Customer Support, of the MATRIXx Getting Started Guide for complete
instructions on using the help feature.
µ
Module
Chapter 1 Introduction
Overview
The Xmath Model Reduction Module (MRM) provides a collection of tools
for reducing the order of systems. Many of the functions are based on
state-of-the-art algorithms in conjunction with researchers at the Australian
National University, who were responsible for the original development of
some of the algorithms. A common theme throughout the module is the use
of Hankel singular values and balanced realizations, although
considerations of numerical accuracy often dictates whether these tools are
used implicitly rather than explicitly. The tools are particularly suitable
when, as generally here, quality of approximation is measured by closeness
of frequency domain behavior.
© National Instruments Corporation 1-3 Xmath Model Reduction Module
Chapter 1 Introduction
Functions
As shown in Figure 1-1, functions are provided to handle four broad tasks:
• Model reduction with additive errors
• Model reduction with multiplicative errors
• Model reduction with frequency weighting of an additive error,
including controller reduction
• Utility functions
Additive Error
Model Reduction
balmoore
redschur
ophank
truncate
balance
mreduce
Multiplicative
Model Reduction
bst
mulhank
Utility Functions
hankelsv
stable
compare
Figure 1-1. MRM Function
Frequency Weighted
Model Reduction
wtbalance
fracred
Xmath Model Reduction Module 1-4 ni.com
Chapter 1 Introduction
Certain restrictions regarding minimality and stability are required of the
input data, and are summarized in Table 1-1.
Table 1-1. MRM Restrictions
balance( )
balmoore ( ) A state-space system must be stable and minimal,
A stable, minimal system
having at least one input, output, and state
bst( ) A state-space system must be linear,
continuous-time, and stable, with full rank along
the jω-axis, including infinity
compare( ) Must be a state-space system
fracred( )
hankelsv( ) A system must be linear and stable
mreduce( ) A submatrix of a matrix must be nonsingular
A state-space system must be linear and continuous
for continuous systems, and variant for discrete
systems
mulhank( )
A state-space system must be linear,
continuous-time, stable and square, with full
rank along the jω-axis, including infinity
ophank( )
A state-space system must be linear,
continuous-time and stable, but can be nonminimal
redschur( ) A state-space system must be stable and linear,
but can be nonminimal
stable ( ) No restriction
truncate( )
wtbalance( )
Any full-order state-space system
A state-space system must be linear and
continuous. Interconnection of controller and plant
must be stable, and/or weight must be stable.
Documentation of the individual functions sometimes indicates how the
restrictions can be circumvented. There are a number of model reduction
methods not covered here. These include:
• Padé Approximation
• Methods based on interpolating, or matching at discrete frequencies
© National Instruments Corporation 1-5 Xmath Model Reduction Module
Chapter 1 Introduction
Nomenclature
• L2 approximation, in which the L2 norm of impulse response error (or,
by Parseval’s theorem, the L
norm of the transfer-function error along
2
the imaginary axis) serves as the error measure
• Markov parameter or impulse response matching, moment matching,
covariance matching, and combinations of these, for example,
q-COVER approximation
• Controller reduction using canonical interactions, balanced Riccati
equations, and certain balanced controller reduction algorithms
This manual uses standard nomenclature. The user should be familiar with
the following:
• sup denotes supremum, the least upper bound.
• The acute accent (´) denotes matrix transposition.
• A superscripted asterisk (*) denotes matrix transposition and complex
conjugation.
• λ
(A) for a square matrix A denotes the maximum eigenvalue,
max
presuming there are no complex eigenvalues.
•Reλ
(A) and |λi(A)| for a square matrix A denote an arbitrary real part
i
and an arbitrary magnitude of an eigenvalue of A.
Xjω()
• for a transfer function X(·) denotes:
∞
sup
∞– ω∞<<
λ
X*jω()Xjω()[][]
max
12/
• An all-pass transfer-function W(s) is one where for all ω;
Xjω() 1=
to each pole, there corresponds a zero which is the reflection through
the jω-axis of the pole, and there are no jω-axis poles.
• An all-pass transfer-function matrix W(s) is a square matrix where
W′ jω–()Wjω() I=
P >0 and P ≥ 0 for a symmetric or hermitian matrix denote positive
and nonnegative definiteness.
> P2 and P1≥ P2 for symmetric or hermitian P1 and P2 denote
• P
1
P
– P2 is positive definite and nonnegative definite.
1
•A superscripted number sign (#) for a square matrix A denotes the
Moore-Penrose pseudo-inverse of A.
Xmath Model Reduction Module 1-6 ni.com
• An inequality or bound is tight if it can be met in practice, for example
1 xx– 0≤log+
is tight because the inequality becomes an equality for x =1. Again,
if F(jω) denotes the Fourier transform of some , the
Heisenberg inequality states,
ft()
-------------------------------------------------------------------------------------------
2
t
ft()2dt
∫
and the bound is tight since it is attained for f(t) = exp + (–kt
∫
12⁄
∫
Commonly Used Concepts
This section outlines some frequently used standard concepts.
Controllability and Observability Grammians
Suppose that G(s)=D + C(sI–A)–1B is a transfer-function matrix with
Reλ
(A)<0. Then there exist symmetric matrices P, Q defined by:
i
2
dt
2
ω
Fjω()2dω
Chapter 1 Introduction
ft() L
∈
2
4π≤
12⁄
2
).
PA′ +AP = –BB′
QA + A′Q = –C′C
These are termed the controllability and observability grammians of the
realization defined by {A,B,C,D}. (Sometimes in the code, WC is used for
P and WO for Q.) They have a number of properties:
• P ≥ 0, with P > 0 if and only if [A,B] is controllable, Q ≥ 0 with Q >0
if and only if [A,C] is observable.
∞
• and
PeAtBB′e
= Qe
∫
• With vec P denoting the column vector formed by stacking column 1
• The controllability grammian can be thought of as measuring the
© National Instruments Corporation 1-7 Xmath Model Reduction Module
0
of P on column 2 on column 3, and so on, and ⊗ denoting Kronecker
product
difficulty of controlling a system. More specifically, if the system is in
the zero state initially, the minimum energy (as measured by the L
norm of u) required to bring it to the state x
eigenvalues of P correspond to systems that are difficult to control,
while zero eigenvalues correspond to uncontrollable systems.
A′ t
dt
IAAI⊗+⊗[]vecP vec(– BB′)=
∞
A′ t
∫
0
C′CeAtdt
0
is x0P –1x0; so small
2
=
Chapter 1 Introduction
• The controllability grammian is also E[x(t)x′(t)] when the system
x·Ax Bw+=
noise with .
has been excited from time –∞ by zero mean white
Ewt()w′ s()[]Iδ ts–()=
• The observability grammian can be thought of as measuring the
information contained in the output concerning an initial state.
x·Ax= y, Cx=
If with x(0) = x
∞
y′ t()yt()dt
∫
0
then:
0
=
x′0Qx
0
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 |λi(A)|<1, P and Q are:
P – APA′ = BB′
Q – A′QA = C′C
The first dot point above remains valid. Also,
∞
k
• and
= QA
PA
∑
k 0=
BB′A ′
k
∞
=
k
∑
k 0=
C′CA′
k
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
• 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
then P and Q are replaced by TPT′ and (T
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.
Xmath Model Reduction Module 1-8 ni.com
1/2
(PQ). Notice the following:
i
–1
–1
)′QT–1; then PQ is replaced
, TB, CT
–1
and D,
Chapter 1 Introduction
• Suppose the transfer-function matrix corresponds to a discrete-time
system, with state variable dimension n. Then the infinite Hankel
matrix,
2
B
2
B
H
CB CAB CA
CAB CA
=
2
CA
B
has for its singular values the n nonzero Hankel singular values,
together with an infinite number of zero singular values.
The Hankel singular values of a (stable) all pass system (or all pass matrix)
are all 1.
Slightly different procedures are used for calculating the Hankel singular
values (and so-called weighted Hankel singular values) in the various
functions. These procedures are summarized in Table 1-2.
Table 1-2. Calculating Hankel Singular Values
(balance( ))
balmoore( )
hankelsv( )
ophank( )
redschur( )
bst( )
mulhank( )
wtbalance( )
fracred( )
For a discussion of the balancing algorithm, refer to
the Internally Balanced Realizations section; the
Hankel singular values are given by
1/2
diag(R
)=HSV
For a discussion of the balancing algorithm, refer to
the Internally Balanced Realizations section; the
matrix S
diag(SH)
real(sqrt(eig(p*q)))
yields the Hankel singular values through
H
Calls hankelsv( )
Computes a Schur decomposition of P*Q and then
takes the square roots of the diagonal entries
Same as redschur( ) except either P or Q can be
a weighted grammian
© National Instruments Corporation 1-9 Xmath Model Reduction Module
Chapter 1 Introduction
Internally Balanced Realizations
Suppose that a realization of a transfer-function matrix has the
controllability and observability grammian property that P = Q = Σ for
some diagonal Σ. Then the realization is termed internally balanced. Notice
that the diagonal entries σ
that is, they are the Hankel singular values. Often the entries of Σ are
assumed ordered with σ
As noted in the discussion of grammians, systems with small (eigenvalues
of) P are hard to control and those with small (eigenvalues of) Q are hard
to observe. Now a state transformation T = α I will cause P = Q to be
replaced by α
expense of difficulty of observation, and conversely. Balanced realizations
are those when ease of control has been balanced against ease of
observation.
Given an arbitrary realization, there are a number of ways of finding a
state-variable coordinate transformation bringing it to balanced form.
A good survey of the available algorithms for balancing is in [LHPW87].
One of these is implemented in the Xmath function
2
P, α–2Q, implying that ease of control can be obtained at the
of Σ are square roots of the eigenvalues of PQ,
i
≥ σ
.
i
i+1
balance( ).
The one implemented in
balmoore( ) as part of this module is more
sophisticated, but more time consuming. It proceeds as follows:
1. Singular value decompositions of P and Q are defined. Because P and
Q are symmetric, this is equivalent to diagonalizing P and Q by
orthogonal transformations.
P=U
Q=UoSo U′
cSc
U′
c
o
2. The matrix,
12⁄
=
HS
0
UHSHV
12⁄
H
is constructed, and from it, a singular value decomposition is obtained:
=
HU
′
HSHVH
3. The balancing transformation is given by:
=
TU
The balanced realization is T
Xmath Model Reduction Module 1-10 ni.com
12⁄–
0S0
–1
AT, T–1B, CT.
UHS
12⁄
H
Chapter 1 Introduction
This is almost the algorithm set out in Section II of [LHPW87]. The one
difference (and it is minor) is that in [LHPW87], lower triangular Cholesky
factors of P and Q are used, in place of U
cSc
1/2
and UOS
1/2
in forming H
O
in step 2. The grammians themselves need never be formed, as these
Cholesky factors can be computed directly from A, B, and C in both
continuous and discrete time; this, however, is not done in
balmoore.
The algorithm has the property that:
Thus the diagonal entries of S
The algorithm implemented in
A lower triangular Cholesky factor L
Then the symmetric matrix L
value decomposition), thus L′
the coordinate basis transformation is given by T = L
T′QT = T
Singular Perturbation
A common procedure for approximating systems is the technique of
Singular Perturbation. The underlying heuristic reasoning is as follows.
Suppose there is a system with the property that the unforced responses of
the system contain some modes which decay to zero extremely fast. Then
an approximation to the system behavior may be attained by setting state
variable derivatives associated with these modes to zero, even in the forced
case. The phrase “associated with the modes” is loose: exactly what occurs
is shown below. The phrase “even in the forced case” captures a logical
flaw in the argument: smallness in the unforced case associated with initial
conditions is not identical with smallness in the forced case.
Suppose the system is defined by:
–1
P(T–1)′ = R
T′ QT′ T
1/2
.
1–
PT1–()′ S
==
are the Hankel singular values.
H
balance( ) is older, refer to [Lau80].
of P is found, so that LcLc′ = P.
c
′QLc is diagonalized (through a singular
c
= VRU′, with actually V = U. Finally,
cQLc
H
VR
c
–1/4
, resulting in
·
x
·
x
© National Instruments Corporation 1-11 Xmath Model Reduction Module
A11A
1
A21A
2
y
C
1C2
x
B
1
12
x
22
x
1
x
2
1
u+=
B
2
2
Du+=
(1-1)
Chapter 1 Introduction
and also:
Reλ
)<0 and .
i(A22
Reλ
iA11A12A22
–()0<
1–
A
21
Usually, we expect that,
()ReλiA11A12A
Reλ
iA22
–()«
1–
A
22
21
in the sense that the intuitive argument hinges on this, but it is not necessary.
·
Then a singular perturbation is obtained by replacing by zero; this
x
2
means that:
A21x1A22x2B2u++ 0=
or
x
2
1–
A–
A21x1A
22
1–
B2u–=
22
Accordingly,
·
x
A11A12A
1
yC
–()x1DC2A
1C2A22
1–
=()x1B1A12A
A
22
21
1–
A
21
–()u+=
–()u+=
1–
B
22
2
1–
B
22
2
(1-2)
Equation 1-2 may be an approximation for Equation 1-1. This means that:
• The transfer-function matrices may be similar.
• If Equation 1-2 is excited by some u(·), with initial condition x
1(to
), and
if Equation 1-1 is excited by the same u(·) with initial condition given
by,
•x
1(to
then x
) and x2(to) = –A
(·) and y(·) computed from Equation 1-1 and from Equation 1-2
1
–1
A21x1(to)–A
22
–1
22
B2u(to),
should be similar.
• If Equation 1-1 and Equation 1-2 are excited with the same u(·), have
the same x
) and Equation 1-1 has arbitrary x2, then x1(·) and y(·)
1(to
computed from Equation 1-1 and Equation 1-2 should be similar after
a possible initial transient.
As far as the transfer-function matrices are concerned, it can be verified that
they are actually equal at DC.
Xmath Model Reduction Module 1-12 ni.com
Chapter 1 Introduction
Similar considerations govern the discrete-time problem, where,
can be approximated by:
mreduce( ) can carry out singular perturbation. For further discussion,
refer to Chapter 2, Additive Error Reduction. If Equation 1-1 is balanced,
singular perturbation is provably attractive.
Spectral Factorization
Let W(s) be a stable transfer-function matrix, and suppose a system S with
transfer-function matrix W(s) is excited by zero mean unit intensity white
noise. Then the output of S is a stationary process with a spectrum Φ(s)
related to W(s) by:
x
k 1+()
1
k 1+()
x
2
A
11A12
A21A
22
x1k()
x
2
x1k()
yk()
x1k 1+()A11A12IA
y
k
C
1C2
x
k()
2
+[]x1k()+=
B
+[]uk()
1A12
C1C2IA
+[]x1k()+=
DC
+[]uk()
–()
IA
–()
–()
22
IA
–()
2
22
22
22
k()
1–
1–
B
1
uk()+=
B
2
Du k()+=
1–
A
21
1–
B
2
A
21
B
2
Φ s() Ws()W′ s–()=
(1-3)
Evidently,
Φ jω() Wjω()W
*
jω()=
so that Φ( jω) is nonnegative hermitian for all ω; when W( jω) is a scalar, so
is Φ( jω) with Φ( jω)=|W( jω)|
2
.
In the matrix case, Φ is singular for some ω only if W does not have full
rank there, and in the scalar case only if W has a zero there.
Spectral factorization, as shown in Example 1-1, seeks a W(jω), given
Φ(jω). In the rational case, a W(jω) exists if and only if Φ(jω) is
© National Instruments Corporation 1-13 Xmath Model Reduction Module
Chapter 1 Introduction
nonnegative hermitian for all ω. If Φ is scalar, then Φ(jω)≥0 for all ω.
Normally one restricts attention to Φ(·) with lim
Φ(jω)<∞. A key result
ω→∞
is that, given a rational, nonnegative hermitian Φ(jω) with
lim
Φ(jω)<∞, there exists a rational W(s) where,
ω→∞
• W(∞)<∞.
• W(s) is stable.
• W(s) is minimum phase, that is, the rank of W(s) is constant in Re[s]>0.
In the scalar case, all zeros of W(s) lie in Re[s]≤0, or in Re[s]<0 if Φ(jω)>0
for all ω.
In the matrix case, and if Φ(jω) is nonsingular for some ω, it means that
W(s) is square and W
is nonsingular for all ω.
Moreover, the particular W(s) previously defined is unique, to within right
multiplication by a constant orthogonal matrix. In the scalar case, this
means that W(s) is determined to within a ±1 multiplier.
Example 1-1 Example of Spectral Factorization
Suppose:
Then Equation 1-3 is satisfied by , which is stable and
minimum phase.
Also, Equation 1-3 is satisfied by and , and , and
so forth, but none of these is minimum phase.
bst( ) and mulhank( ) both require execution within the program of
a spectral factorization; the actual algorithm for achieving the spectral
factorization depends on a Riccati equation. The concepts of a spectrum
and spectral factor also underpin aspects of
–1
(s) has all its poles in Re[s]≤ 0, or in Re[s]<0 if Φ(jω)
2
1+
Ws()
s 1–
----------s 2+
ω
---------------=
2
ω
4+
s 1+
-----------
±=
s 2+
s 3–
----------s 2+
wtbalance( ).
s 1–
-----------
s 2+
s 1+
sT–
-----------
e
s 2+
Φ jω()
Xmath Model Reduction Module 1-14 ni.com
Chapter 1 Introduction
Low Order Controller Design Through Order Reduction
The Model Reduction Module is particularly suitable for achieving low
order controller design for a high order plant. This section explains some of
the broad issues involved.
Most modern controller design methods, for example, LQG and H
∞, yield
controllers of order roughly comparable with that of the plant. It follows
that, to obtain a low order controller using such methods, one must either
follow a high order controller design by a controller reduction step,
or reduce an initially given high order plant model, and then design a
controller using the resulting low order plant, with the understanding that
the controller will actually be used on the high order plant. Refer to
Figure 1-2.
High Order Plant
Plant
Reduction Reduction
Low Order Plant
Figure 1-2. Low Order Controller Design for a High Order Plant
High Order Controller
Controller
Low Order Controller
Generally speaking, in any design procedure, it is better to postpone
approximation to a late step of the procedure: if approximation is done
early, the subsequent steps of the design procedure may have unpredictable
effects on the approximation errors. Hence, the scheme based on high order
controller design followed by reduction is generally to be preferred.
Controller reduction should aim to preserve closed-loop properties as far
as possible. Hence the controller reduction procedures advocated in this
module reflect the plant in some way. This leads to the frequency weighted
reduction schemes of
wtbalance( ) and fracred( ), as described in
Chapter 4, Frequency-Weighted Error Reduction. Plant reduction logically
should also seek to preserve closed-loop properties, and thus should involve
the controller. With the controller unknown however, this is impossible.
Nevertheless, it can be argued, on the basis of the high loop gain property
within the closed-loop bandwidth that is typical of many systems, that
© National Instruments Corporation 1-15 Xmath Model Reduction Module
Chapter 1 Introduction
multiplicative reduction, as described in Chapter 4, Frequency-Weighted
Error Reduction, is a sound approach. Chapter 3, Multiplicative Error
Reduction, and Chapter 4, Frequency-Weighted Error Reduction, develop
these arguments more fully.
Xmath Model Reduction Module 1-16 ni.com
Additive Error Reduction
This chapter describes additive error reduction including discussions of
truncation of, reduction by, and perturbation of balanced realizations.
Introduction
Additive error reduction focuses on errors of the form,
2
Gjω()G
where G is the originally given transfer function, or model, and G
reduced one. Of course, in discrete-time, one works instead with:
Gejω()Grejω()–
As is argued in later chapters, if one is reducing a plant that will sit inside
a closed loop, or if one is reducing a controller, that again is sitting in a
closed loop, focus on additive error model reduction may not be
appropriate. It is, however, extremely appropriate in considering reducing
the transfer function of a filter. One pertinent application comes specifically
from digital filtering: a great many design algorithms lead to a finite
impulse response (FIR) filter which can have a very large number of
coefficients when poles are close to the unit circle. Model reduction
provides a means to replace an FIR design by a much lower order infinite
impulse response (IIR) design, with close matching of the transfer function
at all frequencies.
jω()–
r
∞
is the
r
∞
© National Instruments Corporation 2-1 Xmath Model Reduction Module
Chapter 2 Additive Error Reduction
Truncation of Balanced Realizations
A group of functions can be used to achieve a reduction through truncation
of a balanced realization. This means that if the original system is
·
x
·
x
A11A
1
2
A21A
12
22
x
B
1
1
x
2
u+=
B
2
y
C
1C2
xD
+=
u
(2-1)
and the realization is internally balanced, then a truncation is provided by
·
x
A11x1B1u+=
1
1x1
Du+=
yC
The functions in question are:
• balmoore( )
• balance( ) (refer to the Xmath Help)
• truncate( )
• redschur( )
One only can speak of internally balanced realizations for systems which
are stable; if the aim is to reduce a transfer function matrix G(s) which
contains unstable poles, one must additively decompose it into a stable part
and unstable part, reduce the stable part, and then add the unstable part back
in. The function
stable( ), described in Chapter 5, Utilities, can be used
to decompose G(s). Thus:
G(s) = G
(s) + Gu(s)(Gs(s) stable, Gu(s) unstable)
s
Gsr(s) = found by algorithm (reduction of Gs(s))
(s) = Gsr(s) + Gu(s) (reduction of G(s))
G
r
Xmath Model Reduction Module 2-2 ni.com
Chapter 2 Additive Error Reduction
A very attractive feature of the truncation procedure is the availability
of an error bound. More precisely, suppose that the controllability and
observability grammians for [Enn84] are
Σ
0
PQΣ
===
1
0 Σ
2
(2-2)
with the diagonal entries of Σ in decreasing order, that is, σ
≥ σ2 ≥ ···. Then
1
the key result is,
with G, G
Gjω()Grjω()–
the transfer function matrices of Equation 2-1 and Equation 2-2,
r
2trΣ
≤
∞
2
respectively. This formula shows that small singular values can, without
great cost, be thrown away. It also is valid in discrete time, and can be
improved upon if there are repeated Hankel singular values. Provided that
the smallest diagonal entry of Σ
of Σ
, the reduced order system is guaranteed to be stable.
2
strictly exceeds the largest diagonal entry
1
Several other points concerning the error can be made:
• The error G( jω)–G
( jω) as a function of frequency is not flat; it is zero
r
at ω = ∞, and may take its largest value at ω = 0, so that there is in
general no matching of DC gains of the original and reduced system.
• The actual error may be considerably less than the error bound at all
frequencies, so that the error bound formula can be no more than an
advance guide. However, the bound is tight when the dimension
reduction is 1 and the reduction is of a continuous-time
transfer-function matrix.
• With g(·) and g
responses of G and G
(·) denoting the impulse responses for impulse
r
and with Gr of degree k, the following L1 bound
r
holds [GCP88]
gg
–
This bound also will apply for the L
≤
r
1
42k 1+()trΣ
error on the step response.
∞
2
It is helpful to note one situation where reduction is likely to be difficult (so
that Σ will contain few diagonal entries which are, relatively, very small).
Suppose G(s), strictly proper, has degree n and has (n – 1) unstable zeros.
Then as ω runs from zero to infinity, the phase of G(s) will change by
(2n – 1)π/2. Much of this change may occur in the passband. Suppose G
r
(s)
has degree n–1; it can have no more than (n – 2) zeros, since it is strictly
© National Instruments Corporation 2-3 Xmath Model Reduction Module
Chapter 2 Additive Error Reduction
proper. So, even if all zeros are unstable, the maximum phase shift when ω
moves from 0 to ∞ is (2n – 3)π/2. It follows that if G(jω) remains large in
magnitude at frequencies when the phase shift has moved past (2n – 3)π/2,
approximation of G by G
approximation may depend somehow on removing roughly cancelling
pole-zeros pairs; when there are no left half plane zeros, there can be no
rough cancellation, and so approximation is unsatisfactory.
As a working rule of thumb, if there are p right half plane zeros in the
passband of a strictly proper G(s), reduction to a G
p + 1 is likely to involve substantial errors. For non-strictly proper G(s),
having p right half plane zeros means that reduction to a G
than p is likely to involve substantial errors.
An all-pass function exemplifies the problem: there are n stable poles and
n unstable zeros. Since all singular values are 1, the error bound formula
indicates for a reduction to order n – 1 (when it is not just a bound, but
exact) a maximum error of 2.
Another situation where poor approximation can arise is when a highly
oscillatory system is to be replaced by a system with a real pole.
will necessarily be poor. Put another way, good
r
(s) of order less than
r
(s) of order less
r
Reduction Through Balanced Realization Truncation
This section briefly describes functions that reduce( ), balance( ),
and
truncate( ) to achieve reduction.
balmoore( )—Computes an internally balanced realization of a
•
system and optionally truncates the realization to form an
approximation.
•
balance( )—Computes an internally balanced realization of a
system.
•
truncate( )—This function truncates a system. It allows
examination of a sequence of different reduced order models formed
from the one balanced realization.
redschur( )—These functions in theory function almost the same
•
as the two features of
state-variable realization of a reduced order model, such that the
transfer function matrix of the model could have resulted by truncating
a balanced realization of the original full order transfer function
matrix. However, the initially given realization of the original transfer
function matrix is never actually balanced, which can be a numerically
hazardous step. Moreover, the state-variable realization of the reduced
Xmath Model Reduction Module 2-4 ni.com
balmoore( ). That is, they produce a
Chapter 2 Additive Error Reduction
order model is not one in general obtainable by truncation of an
internally-balanced realization of the full order model.
Figure 2-1 sets out several routes to a reduced-order realization. In
continuous time, a truncation of a balanced realization is again balanced.
This is not the case for discrete time, but otherwise it looks the same.
Full Order Realization
balmoore
(with both steps)
Balanced Realization of
Reduced Order Model
(in continuous time)
Reduced Order Model Transfer Function
balmoore
(with first step)
truncate
balance redschur
Nonbalanced
Realization of
Reduced Order Model
Figure 2-1. Different Approaches for Obtaining the Same Reduced Order Model
Singular Perturbation of Balanced Realization
Singular perturbation of a balanced realization is an attractive way to
produce a reduced order model. Suppose G(s) is defined by,
·
x
·
x
A11A
1
A21A
2
x
B
1
12
x
22
1
u+=
B
2
2
y
© National Instruments Corporation 2-5 Xmath Model Reduction Module
C
1C2
xDu+=
Chapter 2 Additive Error Reduction
with controllability and observability grammians given by,
in which the diagonal entries of Σ are in decreasing order, that is,
σ
1≥σ2
the first diagonal entry of Σ
Reλ
defined by:
Σ
0
PQΣ
===
≥ ···, and such that the last diagonal entry of Σ1 exceeds
. It turns out that Reλi( )<0 and
(A11–A
i
1–
A21)< 0, and a reduced order model Gr(s) can be
A
12
22
2
1
0 Σ
2
1–
A
22
x·A11A12A
yC
1C2A22
The attractive feature [LiA89] is that the same error bound holds as for
balanced truncation. For example,
Although the error bounds are the same, the actual frequency pattern of
the errors, and the actual maximum modulus, need not be the same for
reduction to the same order. One crucial difference is that balanced
truncation provides exact matching at ω = ∞, but does not match at DC,
while singular perturbation is exactly the other way round. Perfect
matching at DC can be a substantial advantage, especially if input signals
are known to be band-limited.
Singular perturbation can be achieved with
the two alternative approaches. For both continuous-time and discrete-time
reductions, the end result is a balanced realization.
Hankel Norm Approximation
1–
–()xB1A12– A
–()xDC2A
Gjω()G
A
22
21
1–
A
21
jω()–
r
+()u+=
1–
–()u+=
22
2trΣ
≤
∞
mreduce( ). Figure 2-1 shows
2
1–
B
22
2
B
2
In Hankel norm approximation, one relies on the fact that if one chooses an
approximation to exactly minimize one norm (the Hankel norm) then the
infinity norm will be approximately minimized. The Hankel norm is
defined in the following way. Let G(s) be a (rational) stable transfer
Xmath Model Reduction Module 2-6 ni.com
Chapter 2 Additive Error Reduction
ˆ
ˆ
function matrix. Consider the way the associated impulse response maps
inputs defined over (–∞,0] in L
into outputs, and focus on the output over
2
[0,∞). Define the input as u(t) for t <0, and set v(t)=u(–t). Define the
output as y(t) for t > 0. Then the mapping is
∞
yt() CexpAt r+()Bv r()dr
=
∫
0
–1
if G(s)=C(sI-A)
G
norm of G. A key result is that if σ
H
values of G(s), then .
B. The norm of the associated operator is the Hankel
≥ ···, are the Hankel singular
G
σ1=
H
1≥σ2
To avoid minor confusion, suppose that all Hankel singular values of G are
distinct. Then consider approximating G by some stable of prescribed
degree k much that is minimized. It turns out that
ˆ
GG
–
H
G
inf
Gˆof degree k
and there is an algorithm available for obtaining . Further, the
optimum which is minimizing does a reasonable job
of minimizing , because it can be shown that
ˆ
G
ˆ
GG
–
∞
GG
–
where n =deg G, with this bound subject to the proviso that G and are
GG
–
GG
–
ˆ
≤
∞
ˆ
σ
k 1+
G()=
H
G
ˆ
H
σ
j
∑
jk1+=
ˆ
G
allowed to be nonzero and different at s = ∞.
The bound on is one half that applying for balanced truncation.
ˆ
GG
–
However,
• It is actual error that is important in practice (not bounds).
• The Hankel norm approximation does not give zero error at ω = ∞
or at ω = 0. Balanced realization truncation gives zero error at ω = ∞,
and singular perturbation of a balanced realization gives zero error
at ω =0.
There is one further connection between optimum Hankel norm
approximation and L
ˆ
with stable and of degree k and with F unstable, then:
G
error. If one seeks to approximate G by a sum + F,
∞
ˆ
G
ˆ
GG
inf
Gˆof degree k and F unstable
© National Instruments Corporation 2-7 Xmath Model Reduction Module
– F–
σ
k 1+
G()=
∞
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