06 model robustness cts
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February 21, 2016 Reliance Technology Group – APC / RTO For Internal Circulation1
Process Engineering GroupProcess Engineering Group
Model RobustnessModel Robustness
Rajalingam RRajalingam R
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1) Importance of Model Robustness
2) Tools for making models robust
) Model Identification ! "M#
FIR Identification
$ubspace identification
%) Model Robustness
Scope
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&'ample
Condition No: 23957 - ILL Conditioned
Condition No: 35 - !ell Conditioned
Solution"#uation
Impo$tance o% Model Robustness
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Model Robustness
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Model Robustness is important from mat(ematical stability
* Model Identification+ Finite Impulse Response FIR) and $ubspace
* FIR is Multiple input and $ingle -utput Model MI$-)
* $ubspace is Multiple input Multiple -utput Model MIM-)
* Model .ncertainty+* Time "omain and Fre/uency "omain nalysis
* #orrelations+
* uto correlation #ross correlation analysis
* #ondition number and R+
* #ondition 3umber * Relati4e ain analysis
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Model IdentifcationModel Identifcation
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Linea$ Models
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$tructure &mpirical9data!based First principles
Mat(ematicalform
:inear9non!linear :inear9non!linear
"'amples o% Linea$ models+
$tatic pressure ;it( respect to dept( of li/uid
<olume of a 4ertical cylinder ;it( respect to le4el
"'amples o% Non-linea$ models:
In distillation column, t(e c(ange in purity ;it( Reboiler "uty ;ill be more ;(en
Reflu' Ratio is small= and 4ice 4ersa
Linea$i(ation
If non!linear, t(en use a suitable transform like :- transform) to make t(erelation linear
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)ene%its o% Linea$ models
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)ene%its o% Linea$ models
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)ene%its o% Linea$ models
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)ene%its o% Linea$ models
@I1 *+ I+,+
I2,+*+
@I2
1
C. C+ C2 C3 C/ C5 C0 C7
+/
70 7 7 7
2+
253 5 5 5
2
90
+2
#<
+2 +2 +2
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Identi%ication
δ #< A B ∆I, IAM<
Cno;n
Calculate
∆∆
∆∆
=
∂∂∂∂∂∂∂
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1
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12%
12
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*
Cno;n
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Response to a c(ange in t(e independent+
#<1 ! #<0 A a1 B DI)
#<2 ! #<0 A a2 B DI)
#< ! #<0 A a B DI)#<% ! #<0 A a% B DI)
#<5 ! #<0 A a5 B DI)
#<6 ! #<0 A a5 B DI)
DIInd
a1a2
aa% a5
Ereser4ation of $cale
#<1
#<2
#<
#<% #<5
#<0
Time 0 1 2 % 5
"ep
Identi%ication
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0 2 % 6 >
Ind 1!2
1
%%
!1
!5
!8!8
!1%
86 8 8 8
!>
!2
!12
!1%
#<
!8,!2)B8,0)B8,1)B8#<#<
!8,!2)B8,0)B8,1)B8#<#<
!5,!2)B6,0)B8,1)B8#<#<
!1,!2)B%,0)B6,1)B8#<#<
%,!2)1B,0)B%,1)B6#<#<
%,0)1B,1)B%#<#<
1,1)1B#<#<
08
06
05
0%
0
02
01
=++=−
=++=−
=++=−
=++=−
=++=−=+=−
==−
Identi%ication
Time 0 1 2 % 5 6 8
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Finite Impulse Response FIR) Identification
ny impulse can be represented like t(is+ G1,0,0,H0
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Finite Impulse Response FIR) Identification
-utput 4ector can be ;ritten as a combination of inputs 4ector+
yJtK A (0 'JtK (1 'Jt!1K (2 'Jt!K HHHH (k!1 'Jt!k!1)K (k 'Jt!kK
* 'JtK L c(ange in independent 4ariable* yJtK L c(ange in dependent 4ariable* k A Time to $teady $tate
* t A time
We usually assume that h0= 0, i.e., the system
does not react immediately to the input.
3o;, by least s/uare obecti4e function, ;e can find
out t(e step response coefficients G(0,(1,(2,H,(k
where N = total number of samples in a dataset
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FIR I"H$tep Response #oefficients
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$tep!Response coefficients
FIR I"Hstep response coefficients
Impulse $esponse coe%%icients :
Step $esponse coe%%icients +
ny step response can be represented as superposition of 4arious impulses+
G1,1,1H,1 A G1,0,0,H,0 G0,1,0,H,0 G0,0,1H,0 H G 0,0,0H,1
G0,(1,(2,(,H,0 G0,0,(1,(2,(H,0 G0,0,0,(1,(2,(H,0 H G 0,0,0H,(n
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FIR Identification+ T(e Matri'
Steps to dete$mine t1e unit step $esponses
4u$ing a test pe$iod, collect time-stamped p$ocess data
%$om t1e sstem %o$ all independent and dependent
6a$iables
&$om t1e collected data, calculate C and I
Sol6e t1e cont$ol e#uation, C 8 I, %o$ t1e unit step
$esponse coe%%icients, t1e mat$i'
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Erocess reac(es steady!state
after % inter4als $o, t(ere are
only % NaO coefficients
FIR Identification
"#uations %o$ p$edicting t1e linea$ sstem;
0B)IIaB)IIaB)IIaB)IIaB)II#<#<
aB)IIaB)IIaB)IIaB)II#<#<
aB)IIaB)IIaB)II#<#<
aB)IIaB)II#<#<
aB)II#<#<
01%1222%1%505
%0112221%0%
01212120
20111202
10101
−+−+−+−+−=−−+−+−+−=−
−+−+−=−−+−=−
−=−
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FIR Identification+ N$tep!ResponseO Form
Step-Response &o$m o% t1e Identi%ication <$oblem
%1222%1%505
%0112221%0%
01212120
20111202
10101
aB)IIaB)IIaB)IIaB)II#<#<
aB)IIaB)IIaB)IIaB)II#<#<
aB)IIaB)IIaB)II#<#<
aB)IIaB)II#<#<aB)II#<#<
−+−+−+−=−−+−+−+−=−
−+−+−=−−+−=−
−=−
∆
∆
∆∆
=
∂
∂
∂
∂∂
∂
∂
%
2
1
%%%%
%%%
2%%
12%
12
12
1
8
6
5
%
2
1
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I
II
aaaa
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a
#<
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#<
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#<
#<
*
∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆
∆∆∆
∆∆∆
=
∂∂∂∂∂
∂∂
%
2
1
%568
%56
2%5
12%
12
12
1
8
6
5
%
2
1
IIII
IIII
IIII
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I
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a
a
aa
*
#omplete e/uations
useful)
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FIR Identification L Pad "ata
period of bad data s(ould not in4alidate t(e entire data set
In t(e step!form of t(e algorit(m
#<0 appears in eac( e/uation
-nly one continuous section of data can be analyQed using t(is algorit(m L step
response algorit(m
Pad data ;it(in a data set in4alidates t(e data set
L Erocess problem9upset
L #omputer problem
L ard;are9instrumentation failure
L <al4e saturation
L typical disturbance
-t(er forms of t(e algorit(m L eliminate #<07
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FIR Identification L Impulse Form 1)
0)))
)))
))
)
+−−+−−+−−+−=−
−−−−−−−−=+
−+−+−+−+−=−
−−+−−+−−+−=−
−−−−−−=+
−+−+−+−=−
−−+−−+−=−−−−−=+
−+−+−=−
−+−=−
−−=−
−+−=−
−=−
%122212%1%5%5
%0112221%0%
%01%1222%1%505
%012121221%%
01212120
%0112221%0%
2011212122
20111202
01212120
120111212
10101
20111202
10101
a,aB)I,Ia,aB)I,Ia,aB)I,IaB)I,I#<#<
aB)I,IaB)I,IaB)I,IaB)I,I#<#<!
aB)I,IaB)I,IaB)I,IaB)I,IaB)I,I#<#<
a,aB)I,Ia,aB)I,Ia,aB)I,IaB)I,I#<#<
aB)I,IaB)I,IaB)I,I#<#<!
aB)I,IaB)I,IaB)I,IaB)I,I#<#<
a,aB)I,Ia,aB)I,IaB)I,I#<#<aB)I,IaB)I,I#<#<!
aB)I,IaB)I,IaB)I,I#<#<
a!,aB)I,IaB)I,I#<#<
aB)I,I#<#<!
aB)I,IaB)I,I#<#<
aB)I,I#<#<
$ubtract pairs of
e/uations to
remo4e #<0
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FIR Identification L Impulse Form 2)
"efine NbiO as t(e impulse coefficient+ ai ! ai!1
3o;, t(e Nimpulse!formO of t(e "M#plus Model e/uations
%1222%1%5%5
%0112221%%
01212122
20111212
10101
bB)IIbB)IIbB)IIbB)II#<#<
bB)IIbB)IIbB)IIbB)II#<#<
bB)IIbB)IIbB)II#<#<
bB)IIbB)II#<#<
bB)II#<#<
−+−+−+−=−−+−+−+−=−
−+−+−=−
−+−=−−=−
%%
2
122
11
aab
aab
aab
ab
−=
−=−=
=
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FIR Identification+ Impulse Response Model
#(aracteristics
Penefit+
"ata $licing is llo;ed+ #(ange in t(e dependent 4ariable is only a
function of t(e past c(anges in independent 4ariables for a time e/ual
to t(e time to steady!state
Eenalty+ dditional noise results from taking t(e deri4ati4e of t(e dependent
4ariable
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FIR Identification+
$imultaneous Independent <ariable Mo4es
In a typical process, it is generally impossible to N(oldO an independent step
for a full time to steady!state ;it(out ot(er mo4ement in ot(er independent
4ariables
-perations needs to make a mo4e
Feedfor;ard, disturbance, 4ariable mo4es
simple, straig(t!for;ard solution to t(e control problem ;ill not (andle
t(ese additional mo4esH
e need a solution met(od t(at is tolerant of multiple mo4es ;it(in a single
Tss
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FIR Identification+
$imultaneous Identification of Models
Multiple Input, $ingle -utput MI$-) $imultaneousidentification
of model
coefficients
∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆
∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆
∆∆∆∆∆∆∆∆∆∆ ∆∆
=
∆∆∆∆
∆∆∆∆∆
2,%
2,
2,2
2,1
1,%
1,
1,2
1,1
2,62,82,>2,?1,61,81,>1,?
2,52,62,82,>1,51,61,81,>
2,%2,52,62,81,%1,51,61,8
2,2,%2,52,61,1,%1,51,6
2,22,2,%2,51,21,1,%1,5
2,12,22,2,%1,11,21,1,%
2,12,22,1,11,21,
2,12,21,11,2
2,11,1
?
>
8
6
5
%
2
1
IIIIIIII
IIIIIIII
IIIIIIIIIIIIIIII
IIIIIIII
IIIIIIII
IIIIII
IIIIII
#<
#<
#<#<
#<
#<
#<
#<#<
b
b
b
b
b
b
b
b
*
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FIR Identification+
$imultaneous Identification of Models
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First, letSs con4ert t(e control e/uation to a
residual form+ C - I 8 R
FIR Identification+ $imultaneous Identification
=
∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆∆
∆∆∆∆∆∆∆∆∆∆
∆∆
−
∆∆∆∆∆∆∆∆
∆
!
"
#
$
%
&
'
(
*
r
r
r
r
r
r
r
r
r
b
b
b
b
b
b
bb
2,%
2,
2,2
2,1
1,%
1,
1,2
1,1
2,62,82,>2,?1,61,81,>1,?
2,52,62,82,>1,51,61,81,>
2,%2,52,62,81,%1,51,61,8
2,2,%2,52,61,1,%1,51,6
2,22,2,%2,51,21,1,%1,5
2,12,22,2,%1,11,21,1,%
2,12,22,1,11,21,
2,12,21,11,2
2,11,1
?
>
8
6
5
%
2
1
IIIIIIII
IIIIIIII
IIIIIIII
IIIIIIII
IIIIIIII
IIIIIIII
IIIIII
IIII
II
#<
#<
#<
#<
#<
#<
#<
#<
#<
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FIR Identification+ $imultaneous Identification L $olution
3o;, ;e can use a N:east $/uaresO regression met(od to sol4e fort(e response coefficients ;(ile minimiQing NR2O
#alculate t(e sum of t(e s/uared residual terms+
Residuals$/uaredof $um=
=
++++++++=
∑ '
'
'
!
'
"
'
#
'
$
'
%
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&
'
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(
i
T
r
r r r r r r r r r r r
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FIR Identification+ $imultaneous Identification L
$ummary
Penefit
$imultaneous solution allo;s for c(anges in more t(an one
independent at a time during t(e test
Implication9Eenalty
Identifying t(e indi4idual responses re/uires uncorrelated mo4ement in
t(e independent 4ariables during t(e plant test
Models are identified simultaneously so c(anging t(e list of
independents may c(ange t(e model coefficients
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FIR Identification "esign #riteria
period of bad data s(ould not in4alidate t(e entire
data set
$ol4e t(e impulse!form rat(er t(an t(e step!form of t(e
algorit(m
More t(an one independent 4ariable s(ould be allo;ed
to c(ange at t(e same time
$ol4e for all independent9#< response coefficients
simultaneously as a least!s/uares MI$- problem
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FIR parameters
+ =ime to stead state ==SS
2 Numbe$ o% coe%%icients
3 Smoot1ing %acto$
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FIR Identification Earameters
Erocess settling time steady!state time)
:engt( of t(e step response models in time
3umber of coefficients
More coefficients re/uired for accurate modeling of fast responses less time
bet;een coefficients)
$moot(ing
Eenalty for c(ange from one coefficient to ne't
Model fit is not significantly degraded
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Puild t(e #ontroller Model+ 3umber of Model #oefficients
T(e number of model coefficients, or t(e number of points used to Ndra;O t(e
line is related to t(e controller e'ecution fre/uency and t(e Tss+
#ontrol inter4al needs to be fast enoug( so t(at t(e controller can respond to
t(e fastest measured9 unmeasured independent disturbances in t(e system
Ideally, t(e controller is running fast enoug( t(at it (as 5 to 10 e'ecutions to (andle
a disturbance before it becomes a problem
−
= Interval
Collection
Data
tsCoefficien Model of Number
StateSteadytoTime
Interval
ExecutionController *
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Puild t(e #ontroller Model+ 3umber of Model #oefficients
f # ff
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FIR parametersH3umber of #oefficients
It decides o4ersampling ratio in model identifications
$ampling time A $ettling time 9 $tep!response coefficients
FIR 3 b f # ffi i
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FIR parametersH3umber of #oefficients
-4ersampling Ratio A 1 -4ersampling Ratio A 2
3otice gain mismatc( in FIR $$ FIR and $$ results no; matc( better
FIR t $ t(i F t
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FIR parametersH$moot(ing Factor
FIR smoot(ing algorit(m ;ill ad4ersely affect model responses ;it( faster initial
dynamics, like 4al4e positions e can use un!smoot(ed response models
instead, and use cur4e operations to remo4e noise and s/uiggles from t(e model
FIR t $ t(i F t
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FIR parametersH$moot(ing Factor
$FA 5 $FA 001
Ti " i (
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Time "omain pproac(
FI I is time domain approach. Consider the followin+ euations-
i/en the compleity of differential euations, why would we e/er want to wor1 in the time
domain2
3he 4aplace transform mo/es us out of the time5domain into the comple freuency domain, so
that
we can study and manipulate our systems as al+ebraic polynomials instead of linear 67s.8( 9 's 9 $s'):8s) = 8( 9 s);8s)
3hat<s ri+ht, the 4aplace transform is hidin+ the fact that we are actually dealin+ with second5order
differential euations. In the 4aplace or time domain, if we want to account for systems with
multiple inputs and multiple outputs, we are +oin+ to need to rely on the principle of
superposition to create a system of simultaneous 4aplace euations 8or time5domain based
euations) for each output and each input. For such systems, the classical approach doesn<tsimplify the situation in MIMO case.
FIR models are $ingle input single output models $I$-) and can be e'tended for
Multiple input single output MI$-) $o, a case containing multiple #<s is run as
combination of MI$- models for eac( #<
$t t (
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$tate!space pproac(
It turns out t(at if ;e decompose our (ig(er!order differential e/uations into multiple first!order
e/uations, ;e can find a ne; met(od for easily manipulating t(e system in MIM> models T(e
solution to t(is problem is state space app$oac1
State
#entral to t(e state!space notation is t(e idea of a state state of a system is t(e current 4alue
of internal elements of t(e system, t(at c(ange separately to but not completely unrelated to)
t(e output of t(e system ere are some e'amples+
*Consider a chemical reaction where certain reagents are poured into a mixing container, and the output is the amount of the
chemical product produced over time. The state variables may represent the amounts of un-reacted chemicals in the container,
or other properties such as the quantity of thermal energy in the container (that can serve to facilitate the reaction).
e denote t(e input 4ariables ;it( u, t(e output 4ariables ;it( y, and t(e state 4ariables ;it( ' In essence,
;e (a4e t(e follo;ing relations(ip+
y A f', u)
(ere f', u) is our system lso, t(e state 4ariables can c(ange ;it( respect to t(e current state and t(e
system input+
' A g', u)
(ere ' is t(e rate of c(ange of t(e state 4ariables
$tate "efinition
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$tate "efinition
$tate 4ariables A memory elements A contains all t(e information from t(e
past t(at is rele4ant for predicting t(e future
If ;e describe system as an operating mapping from t(e space of input to
t(e space of output, t(en ;e may need t(e entire input!output (istory of
t(e systems toget(er ;it( t(e planned input in order to compute future
output 4alues lternati4ely, ;e may use state 4ariables ;(ic( (as all
(istory of input and -utput 4ariables
-rder of model A number of states n)
#< t i i $ b id tifi ti
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#<s uto!grouping in $ubspace identification
$ubspace is MIM- model identification= but ;(en number of unrelated #<s are large in
case, t(en it increases computation time, and benefits ;ere limited compared to running
multiple smaller cases
T(e subspace identification algorit(m can optimiQe case runs by using #< grouping T(e
#< cross correlation is used to determine groups of #< t(at s(are common dynamics,
and create internal sub!case for t(ose related #<s MIM- for eac( internal sub case)
Py keeping related #<s toget(er t(e underlying states ;ill be identified ;it( more
certainty Py remo4ing unrelated #<s t(e computation time per subgroup is decreased
#< grouping alternati4es
1)roup Related #<s uto!grouping, "efault) L ma'imum of 10 #<s for one sub!case
2)-ne #< per roup Forced MI$- I") L ;it( t(is option, t(e users are able to compare
t(e MI$- subspace I" ;it( t(e FIR I" in a similar internal setup
)ll #<s in -ne roup Forced :arge MIM- I") L set as one large MIM-
%)$et Ma'imum #< roup $iQe MIM- I" ;it( certain number #<s per group) L gi4es
fle'ibility to modify t(e default ma'imum #<s of 10 per group
llows users to gain!validate their "nowledge about the C#s correlation in their
process, compare models with different setups, and retune the default parameters when
necessary.
#<s to gro ping in $ bspace identification
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#<s uto!grouping in $ubspace identification
Time to steady state in $ubspace identification
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Time to steady state in $ubspace identification
T(eoretically, TT$$ 4alue (a4e no direct impacts on t(e subspace I", because TT$$ is
not a dependent parameter in subspace I" and it only determines (o; many model cur4e
coefficients ;ill be generated from a subspace model identified
o;e4er, "etrending filters t(at ;ill be calculated upon TT$$ 4alues affect $ubspace I"
for different TT$$
Time to steady state in $ubspace identification
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Time to steady state in $ubspace identification
ll parameters are t(e same
e'cept TT$$
Time to steady state in $ubspace identification
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Time to steady state in $ubspace identification
Ma'imum states per #< group+ $ubspace identification
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Ma'imum states per #< group+ $ubspace identification
$tates do not necessarily (a4e a direct p(ysical interpretations, but t(ey (a4e a
conceptual rele4ance
Ma'imum states per #< group, ;(ic( is an upper bound of t(e model order ie
ma'imum number of states allo;ed) for eac( #< group sub!case
T(is parameters allo;s user to set a model order searc( range, or force t(e subspace
algorit(m to fit a lo;!order model ;it( truncation error
(en to reduce t(e ma'imum states per #< group7
Ma'imum states per #< group+ $ubspace identification
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Ma'imum states per #< group+ $ubspace identification
Ma'imum states per #< group+ $ubspace identification
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Ma'imum states per #< group+ $ubspace identification
ig(!fre/uency dynamic be(a4ior of model is caused by a (ig( model order, and a model
reduction can (elp to damp t(e (ig( fre/uency noise
Ma'imum states per #< group+ $ubspace identification
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Ma'imum states per #< group+ $ubspace identification
Ma'imum order per I9- pair+ $ubspace identification
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Ma'imum order per I9- pair+ $ubspace identification
T(is parameter is t(e identification (oriQon used in t(e identification It is e'actly
t(e number of data points in t(e future and past data (oriQon T(e larger t(e
Ma'imum order per I9- pair, t(e (ig(er order model ;ill be identified in order toco4er longer comple') dynamics T(e computation ;ill also be (ea4ier
Ma'imum order per I9- pair+ $ubspace identification
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Ma'imum order per I9- pair+ $ubspace identification
If 4ery different ma'imum order per I9- are used in subspace case, t(en e4en if t(e same
final model order is selected, t(e calculated models are bound to be different
T(e amount of data used for initialiQing t(e identification run depends on ma'imum order
not t(e final order it e4entually selects), so if you select a (ig( ma' order, more data is
lost If useful data is lost, eg t(e only big step you (a4e) t(en t(e model can degrade If
bad data is lost, t(en t(e model can actually impro4e $o as before ;it( "MI, be careful
;it( slicing t least you lose muc( less data t(an before
:ets assume ma' order A n T(en n pre4ious n data points are t(en used to predict t(ene't n steps into t(e future T(e cost function is determined from t(e difference bet;een
predicted #<s and obser4ed #<s o4er t(ese n steps T(is means t(at a (ig( ma' order
fa4ors t(e long term prediction accuracy, ;(ile lo;er ma' orders ;ill fa4or t(e s(ort term
prediction accuracy of t(e models
Ma'imum order per I9- pair+ $ubspace identification
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Ma'imum order per I9- pair+ $ubspace identification
Ma'imum order per I9- pair+ $ubspace identification
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Ma'imum order per I9- pair+ $ubspace identification
Ma'imum order per I9- pair+ $ubspace identification
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Ma'imum order per I9- pair+ $ubspace identification
"rift Remo4al ! $ubspace
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"rift Remo4al $ubspace
ll process units are disturbed by
se/eral drift disturbances.
rift disturbances are 1ind of
disturbances for which you don’tha/e measure, and it is not possible to
include them as FF si+nal. >ecause of
these drift disturbances, your process
+ain chan+es o/er time.
7ample- in heat echan+er, process
stream is bein+ heated by ?@ steam.
Now, heat echan+er will be
pro+ressi/ely foulin+. Ao o/er a
period time , heat transfer coefficient
will come down, and for the same
amount of steam chan+e 8say (tph)Btemperature chan+e of the outlet
process steam will not be the same
8will be the less as more and more
foulin+ happens).
"ata Ere!processing+ $ubspace identification
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"ata Ere processing+ $ubspace identification
4ailable option on data preprocessing are listed belo;+
1)"etrending
2)"ifferencing
)Uero!mean
%)"ouble "iff
Detrending- @rocess test data often contains low freuency drift that is introduced by un1nown disturbances. 3hese low
freuency disturbances will create ne+ati/e impacts on identification and has to be eliminated. 3he option “etrendin+” is
desi+ned to do that ob.
ssume a measured si+nal can be di/ided into a process si+nal and a trend si+nal.
3he pre5process of data with etrendin+ will estimate the trend si+nal and remo/e it from the system . >y default, the time5
constant of etrendin+ filter is calculated based on the caseDs 33AA-
>asis for &*33AA and #*33AA 5 how much etrendin+ filter should be used2
If central average is calculated over a period of 3*TTSS t!en filter operation tends to remove only slo" drift
disturbances and leave dynamic information mostly intact# $ too small TTSS value for a case may lead to an “ over%
Detrending ” t!at may remove useful dynamic information from t!e data and cause inaccurate models#
"ata Ere!processing+ $ubspace identification
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"ata Ere processing+ $ubspace identification
"ata Ere!processing+ $ubspace identification
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"ata Ere processing+ $ubspace identification
"ifferencing+ yA't1) L 't)Aimilar to etrendin+, differencin+ is also intended to remo/e slow drifts from the process data. 3he
FI model I has been usin+ differencin+ as a data pre5processin+ strate+y since the ?C was first
built. ifferencin+ can remo/e the slow disturbance, but it reduces Ai+nal to Noise 8AEN) ratio.
3herefore, for better model +ains and less strin+ent reuirements on AEN, the etrendin+ is
recommended in place of differencin+.
Uero mean+ It simply remo/es only an offset from raw data. It should be aware that there will be
multiple offsets if data consists of se/eral slices. 3he ero5mean operation will be performed on each
data slices.
"ata Ere!processing+ $ubspace identification
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p g p
"ouble "ifferencing+ Q A y t1) L y t) A 't1)! 't) L 't) ' t!1)
A 't1) L 2 't) ' t!1)
"ouble differencing ;as originally designed for Ramp and Eseudo!Ramp #< in FIR
I" operation For subspace I", it is no longer t(e only ;ay to preprocess Ramp
#<s &it(er "ifferencing or "ouble "ifferencing as an option ;as selected, a single
"ifferencing ;ill be first applied to a Ramp or Eseudo!Ramp #<
In addition, Ramp #< or Eseudo!Ramp #< are al;ays put into one or more Ramp
#< group by t(e uto!grouping algorit(m in order to separate Ramp #<s from non!
Ramp #<s
-4er $ampling Ratio+ $ubspace identification
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p g p
In t(e case ;(ere t(e process data are collected in a
(ig( fre/uency sampling and t(e final model for on!linecontrol ;ill be run in slo;er control sampling fre/uency, a
4alue of greater t(an one can be used to matc( t(e
modelSs sampling rate ;it( t(e controllersS
measurements &g L # analyQers
-4er $ampling Ratio+ $ubspace identification
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p g p
T(e model you get by setting -4er!$ampling Ratio A 1 ;ill
(a4e less precise gain but less delay ;it( t(e dynamics
"ynamic delay means t(e model calculated by $$ isslo;er t(an real system
:ast cur4e is ;it(
$RA10, and it is laggingbe(ind $RA1 cur4e
"irect Term "!matri')+ $ubspace identification
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) p
#ase ;it( non!e'istent in4erse response
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#ase ;it( non e'istent in4erse response
#ase ;it( non!e'istent initial kick
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In <al4e -9E, " term must
"ata $licing L FIR 4s $ubspace
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g p
In subspace algorit(m, (o; muc( data loss ;ill (appen on data
slicing depends on t(e ma'imum order n) per I9- pair NnO data pointsare lost after eac( bad slice
In FIR algorit(m, (o; muc( data loss ;ill (appen on data slicing
depends on t(e time to steady state Tss) "ata of time period e/ual
to one Time to $teady state is lost after eac( bad slice
Identification speed L FIR 9$$ I"
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p
* $ubspace identification takes more computation time t(an t(e FIR
identification, in particular for models ;it( a large number of independent
and dependent 4ariables
*T(is occurs because t(e subspace identification does a true multi!input
multi!output MIM-) model identification t(at needs more intensi4e
computation t(an t(e multi!input single!output MI$-) FIR identification
*T(e benefit is t(at a true MIM- model (as less uncertainty, and is
statistically more accurate
*T(e computation time (as a cubical relation ;it( t(e NMa'imum -rderO
FIR I" 9 $$ I" $ummary
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* FIR algorit(m is like cur4e!fitting non!parametric) , $ubspace
algorit(m is finding parameter for pre!defined model structure
* utomatic model order determination! $$ I" auto!determines t(eoptimal model order, to capture balanced (ig( and lo; fre/uency
dynamics , ;it(out o4er!fitting= ;(ereas in FIR I" model order is pre!
specified based on TT$$ and number of coefficients T(e goodness of
data fitting is t(e only obecti4e of FIR I"
*FIR is MI$-, $$ is truly MIM-*FIR fails ;(en $93 is lo;, $$ performs better *&fficient $licing L $$ I" is more efficient in term of slicing
o;e4er, because of (ig( degree of freedom t(at a FIR model can
offer ;(en fitting a model to a dataset, t(e FIR models can easily
capture (ig( order effects
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Model RobustnessModel Robustness
Model .ncertainty
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When we measure some physical quantity with an instrument and obtain anumerical value, we want to know how close this value is to the true value.
The difference between the true value and the measured value is the error.Unfortunately, the true value is in general unknown and unknowable. Sincethis is the case, the exact error is never known. We can only estimate error.
=1e$e?s no suc1 t1ing as a pe$%ect measu$ement@@=1e$e?s no suc1 t1ing as a pe$%ect measu$ement@@
Pode Elot L Fre/uency "omain .ncertainty
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Pode plot or fre/uency response) of any dynamic system, is defined as t(e
magnitude same as amplitude) of t(e sine ;a4e obser4ed in a specific #<, for a
constant fre/uency sine ;a4e ;it( a peak amplitude of 10 in t(e particular M< st(e fre/uency of sine ;a4e 4aries from t(e real slo; smaller TT$$) to 4ery fast
larger TT$$), t(e amplitude of t(e sine ;a4e 4aries in t(e #< significantly
* Input+ ct) A sinVt
* For simple first order systems) A Cp9Ws1)
* -utput ;ill be + yt) A S sinVt X)
* (ere S A f , V,W, Cp)
Pode Elot L Fre/uency "omain .ncertainty
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Time "omain .ncertainty
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.ncertainty Results
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.ncertainty Results
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.ncertainty Results
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T(e first in4erse response is in red Qone, and it is good to appro'imate
as dead time
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Importance of Model .ncertainty analysis during $tep Test+1) .sed to monitor (o; accurate step test is going on
2) .sed to find out ;(en to slice out data
) Retuning step siQe
%) .sed to find out missing Feed for;ard 4ariables
5) .sed to take decision to ;indup step test
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$tep Test Monitoring
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Retuning $tep $iQe
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$tep test completion
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ddition 9 "eletion of Feed for;ard 4ariables
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