Internati
o
nal
Journal of Ele
c
trical
and Computer
Engineering
(IJE
CE)
V
o
l.
5, N
o
. 5
,
O
c
tob
e
r
201
5, p
p
. 1
054
~106
1
I
S
SN
: 208
8-8
7
0
8
1
054
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJECE
Model Predictive Control System
Design for Boiler Turbine
Process
San
d
eep
Kum
a
r S
u
n
o
ri
*,
Pr
adeep
Ku
mar
June
ja
*
*
,
An
ami
k
a B
h
ati
a
Jai
n
*
**
* Department of
Electronics and
Comm
unication
Engineering, Gr
aphic Er
a Hill
Uni
v
e
r
si
ty
, Bhi
mta
l
Ca
mpus,
Indi
a
**School of
Electronics, Graph
i
c Er
a Univ
ersity
,
Dehradun
, India
***Department of
Electron
i
cs an
d
Communicatio
n, Dehr
adun, Ind
i
a
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Feb 12, 2015
Rev
i
sed
Jun
23,
201
5
Accepte
d
J
u
l 10, 2015
MPC is a computer based
technique th
at r
e
q
u
ires the
proces
s m
odel to
anticipate th
e fu
ture outputs of
that
process. An optimal contr
o
l action is
taken
b
y
MPC
based on
this p
r
edicti
on. Th
e
MPC is so popular sin
c
e its
control p
e
rformance has been r
e
ported
to b
e
best among other
conventional
techn
i
ques to
co
ntrol th
e m
u
ltiv
ariabl
e d
y
nam
i
cal
plants with
vari
ous inputs
and outputs con
s
traints. In th
e
present work th
e control of bo
iler turbin
e
process with three m
a
nipula
t
ed
variab
les nam
e
l
y
fue
l
flow val
v
e position
,
steam
control v
a
lve posit
ion an
d feed water f
l
o
w
valve positio
n and three
controlled var
i
ables namely
dru
m
pr
essure, output power and
drum water
level deviation
has been attempted
using MPC techniqu
e. B
o
iler turb
ine
process is ver
y
com
p
lex and nonline
a
r m
u
ltivar
iabl
e process. A linear
ize
d
model obtained
using Tay
l
or s
e
ries
expansion
around operating point has
been us
ed
.
Keyword:
Bo
iler turb
i
n
e
M
odel
pre
d
i
c
t
i
v
e
c
ont
rol
Mu
ltiv
ariab
l
e p
r
o
cess
No
nl
i
n
ea
r pr
oc
ess
Pred
ictio
n
ho
ri
zo
n
Copyright ©
201
5 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Sand
eep Ku
m
a
r
Su
nor
i,
Depa
rt
em
ent
of El
ect
r
oni
cs
a
n
d
C
o
m
m
uni
cat
i
on E
n
gi
nee
r
i
n
g
,
Graph
i
c Era Hi
ll Un
iv
ersity,
Sattal Ro
ad
, PO: Bho
w
ali, Bh
imta
l, Nain
ital (Uttarak
h
a
nd
), Ind
i
a
Em
a
il: san
d
eepsu
nori@g
m
ai
l.co
m
1.
INTRODUCTION
In
bo
iler turb
i
n
es fl
u
c
tu
ation in
th
e
d
r
u
m
l
e
v
e
l is th
e
b
i
g p
r
ob
lem
as t
h
e steam
flo
w
is d
i
rectly
pr
o
p
o
r
t
i
onal
t
o
po
wer
ge
nera
t
i
on. I
n
case t
h
e d
r
um
l
e
vel
goes
d
o
w
n
t
h
e l
o
wer t
h
res
h
ol
d, t
h
e o
v
er
h
eat
i
n
g
i
n
si
de t
h
e b
o
i
l
e
r
m
a
y
resul
t
in cracki
ng
of
wat
e
r t
ube
s. O
n
t
h
e ot
he
r ha
nd i
f
t
h
e l
e
vel
goes u
p
t
h
e d
e
si
red
up
pe
r t
h
res
hol
d, t
h
e d
r
y
st
eam
fl
owi
ng t
o
t
u
r
b
i
n
e m
a
y carry
som
e
wat
e
r part
i
c
l
e
s whi
c
h m
a
y
dam
a
ge t
h
e
turbine blades
[1]. So
t
h
e
m
a
i
n
ten
a
n
ce
o
f
t
h
e water lev
e
l is th
e cru
c
ial
task
fo
r sa
fe o
p
e
r
ation
o
f
the t
u
rbi
n
e.
The
ot
he
r
para
m
e
t
e
rs t
o
be
c
ont
rol
l
e
d
are t
h
e
dr
um
press
u
re a
n
d t
h
e
ul
t
i
m
a
t
e
object
i
v
e i
s
t
o
m
eet
t
h
e l
o
ad
dem
a
nd o
f
el
ec
t
r
i
c
p
o
we
r.
The M
P
C
cal
cul
a
t
e
s an o
b
j
ec
t
i
v
e fu
nct
i
o
n [
2
]
base
d o
n
t
h
e pre
d
i
c
t
i
on
of
t
h
e out
put
sa
m
p
l
e
s up t
o
a
fixe
d
pre
d
i
c
t
i
o
n h
o
ri
z
o
n an
d t
h
en
det
e
rm
i
n
es t
h
e di
scret
e
m
ove
s o
f
t
h
e i
n
p
u
t
m
a
ni
pul
at
ed
vari
a
b
l
e
s i
n
s
u
ch a
way th
at th
e
o
b
j
ectiv
e
fun
c
tion
is m
i
n
i
m
i
zed
. Th
e MPC st
ra
tegy is elaborat
ed in fi
gure1.
The MPC ta
ke
s control actions at regularly space
d
in
tervals wh
ich
are
called
con
t
ro
l in
terv
als.
As
sh
own
in
fi
g
u
re1
,
th
e con
t
ro
l
l
er p
r
ed
icts th
e o
u
t
p
u
t
v
a
l
u
es
at sa
m
p
lin
g
instan
t t. After
hav
i
ng
co
m
p
leted
th
e
pre
d
i
c
t
i
on cal
c
u
l
a
t
i
ons i
t
sen
d
s m
ove u(t
)
to
t
h
e p
l
an
t
.
The p
l
an
t th
en
operates with
th
i
s
co
nstan
t
inp
u
t u
n
til
th
e n
e
x
t
sam
p
l
i
n
g
in
stan
t. At
th
is sa
m
p
lin
g
in
stan
t t+
1, t
h
e cont
r
o
l
l
e
r a
g
ai
n d
o
es
pred
ictio
n
ov
er pred
ictio
n
ho
ri
zo
n i
n
t
h
e sam
e
way
and agai
n det
e
rm
i
n
es t
h
e new o
p
t
i
m
a
l cont
r
o
l
m
ove
s ove
r t
h
e cont
rol
h
o
ri
z
o
n an
d
this cycle re
pe
ats inde
finitely. T
hus
we
see t
h
at both pre
d
iction a
n
d control horizons a
r
e
receding.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
JECE Vo
l. 5
,
N
o
. 5
,
O
c
tob
e
r
20
15
:
105
4
–
10
61
1
055
Fi
gu
re
1.
M
P
C
st
rat
e
gy
[3]
T. R
a
j
kum
ar et
al
. [3]
used
wi
de o
p
e
n
co
n
t
rol
st
rat
e
gy
w
i
t
h
3 el
em
ent
cont
rol
sy
st
em
t
o
pr
ovi
de
tig
h
t
con
t
ro
l the d
r
u
m
water l
e
v
e
l in
bo
iler tu
rb
in
e. Th
ree
ele
m
en
ts in
d
i
cate 3
v
a
riab
les n
a
m
e
ly stea
m flow,
feed water
flow an
d
d
r
u
m
water lev
e
l
wh
ich
co
llectiv
ely
h
a
s effect
on
feed
water v
a
l
v
e po
sitio
n.
Drum
Lev
e
l
Co
n
t
ro
l Al
go
ri
th
m
was i
m
p
l
e
m
en
ted
in
th
e
Adv
a
n
ced Ad
ap
tiv
e PID C
ontro
ller Blo
c
k
i
n
th
e
FCP270
Field
C
ont
r
o
l
P
r
oces
sor
.
Qu
ick
e
r respon
se and
little
o
v
e
rsh
o
o
t
was o
b
s
erv
e
d
in
th
e resp
on
se of sp
eed
con
t
ro
ller for steam
turbine usi
n
g MPC technique as com
p
ared to
co
nv
en
tio
nal PI
D control
l
er and
fuzzy
cont
roller [4].Om
ar
sh
ah
in
et al. o
b
s
erv
e
d
r
obustn
ess and
satisf
acto
r
y p
e
r
f
or
m
a
n
ce o
f
th
e b
o
iler
tu
rb
ine syste
m
e
m
p
l
o
y
ing
ad
ap
tiv
e
wav
e
let n
e
u
r
al n
e
t
w
ork. Discrete Lyap
un
ov
stab
ility
th
eo
rem was u
s
ed
h
e
re to
determin
e th
e
lear
n
i
ng
r
a
tes [5
].
Spee
d co
nt
r
o
l
of
gas t
u
rbi
n
e sy
st
em
was pr
o
v
ed
to
be outstandi
ng with ada
p
tive fuzzy PID
co
n
t
ro
llers as co
m
p
ared
t
o
con
v
e
n
tion
a
l co
ntro
llers
[6
].
Lin
earization
o
f
a
no
n
lin
ear m
o
d
e
l o
f
boiler tu
rb
in
e
p
l
an
t abo
u
t
a suitab
l
e o
p
e
rating
po
in
t
was
p
r
esen
ted in
t
h
e wo
rk
do
n
e
by
W
e
n Tan
an
d Fang
Fang
et
al.[
7
]
.
Pra
d
eep Kum
a
r J
une
ja et al.
discovere
d
t
h
a
t
if th
e
ratio
o
f
con
t
ro
l ho
rizon
to pred
iction ho
rizo
n
of
an
MPC con
t
roller rem
a
in
s same th
an
its
p
e
rform
a
n
ce alm
o
st rem
a
ins sa
m
e
for a
gi
ven process
[8].
Pan
g
-c
hi
a C
h
e
n
an
d Jef
f
S.
S
h
am
m
a
repo
rt
ed gai
n
sche
d
u
l
e
d l
1
op
tim
al
co
n
t
ro
l fo
r
reg
u
l
ating
th
e
d
r
u
m
water level in
bo
iler t
u
rb
in
es and
ob
tain
ing
th
e d
e
sired
o
u
t
p
u
t
po
wer lev
e
l [9
].
A ne
w co
or
di
nat
e
d co
nt
r
o
l
st
rat
e
gy
(C
C
S
)
has bee
n
p
r
o
p
o
se
d fo
r co
nt
r
o
l
of
boi
l
e
r t
u
rbi
n
e sy
st
em
wh
ich
is ex
ecuted
in
two
lev
e
ls n
a
m
e
l
y
th
e b
a
sic lev
e
l in
wh
ich
conv
en
t
i
o
n
a
l PID con
t
ro
llers are
u
s
ed
to
do
fu
n
d
am
ent
a
l
cont
rol
act
i
on a
nd t
h
e
hi
g
h
l
e
vel
i
n
whi
c
h t
h
e dec
o
u
p
l
i
n
g bet
w
ee
n t
w
o c
ont
rol
l
o
ops i
s
do
ne.
They also used a s
p
ecial class of fuzzy infe
re
nce system
to accom
p
lish self
tuning
[10].
A m
u
lti input s
i
ngle
out
put M
P
C has
bee
n
developed
for a
bioreactor pla
n
t and its
perform
a
nce was
com
p
ared wit
h
that of PID a
nd
fuzzy PID
cont
rolle
r
s
[
11].Fate
m
e Pir
o
uz
m
a
n
d
pr
opo
sed
a ro
bu
st MPC f
o
r
th
ree
d
e
gree freed
o
m
satelli
te
syste
m
s and t
h
e com
p
ensat
i
on
of m
o
m
e
nt
of i
n
ert
i
a
u
n
ce
rt
ai
nt
y
and e
x
t
e
rnal
disturba
nce
wa
s accom
p
lished effec
tively by
this technique
[12].
2.
MPC CONTROLLER DESIGN
T
h
e li
nearize
d
state s
p
ace m
odel of
the considere
d
boiler
t
u
rbine
process with
m
a
nipulated
vari
ables
as fu
el flow
valv
e po
sitio
n (u
1
),
st
eam
fl
ow val
v
e
posi
t
i
on
(
u
2
) a
n
d
fee
d
wate
r fl
ow
v
a
lve(
u
3
) a
n
d
co
nt
r
o
l
l
e
d
vari
a
b
l
e
s as d
r
um
pressu
re(y
1
) i
n
kg/
s
q
.cm
,
out
put
p
o
we
r(
y
2
) in
M
W
and
dru
m
water lev
e
l d
e
v
i
atio
n(y
3
) in
meters with
state varia
b
les a
s
drum
steam
press
u
re(x
1
) in kg/sq.cm
,electric powe
r
(x
2
)
i
n
M
W
a
n
d
s
t
e
a
m
water fluid
de
n
s
ity
(x
3
) i
n
k
g
/
c
ubi
c m
e
t
e
r i
s
r
e
prese
n
t
e
d
by
equat
i
o
ns
(
1
)
,
(
2
)
an
d
(3
)
[1
3]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Mod
e
l Pred
ictive Con
t
ro
l
S
y
st
em
Design
f
o
r
Bo
iler Tu
rb
in
e Pro
c
ess
(Sa
ndeep
K
u
ma
r
Sun
o
r
i)
1
056
A
B
(1
)
C
D
(2
)
A=
0.002854
0
0
0.08315
2
0.1
0
0.007842
0
0
B=
0
.
9
0.374591
0.15
0
15.
191754
0
0
0.148126
1.
658824
C=
100
010
0
.
007566
0
0
.
004257
D=
00
0
00
0
0.2533
0.54305
6
0.014
(3
)
Now t
h
is p
l
an
t
is to
b
e
co
n
t
ro
l
l
ed
b
y
an
M
P
C con
t
ro
ller wit
h
set
po
in
ts
for con
t
ro
lled
v
a
ri
ab
les y
1
, y
2
and y
3
equ
a
l to 12
0 kg
/sq
.
cm
, 12
0 M
W
and
zer
o m
e
ter
s
respectively. T
h
e
3 m
a
nipulated
varia
b
les
u
1
, u
2
and
u
3
have a
val
u
e
ove
r i
n
t
e
r
v
al
[
0
,
1
]
.
The M
P
C
tunin
g
pa
ram
e
ters fo
r the c
o
ntro
ller d
e
si
g
n
are sp
ecified
in
tab
l
e
1
as fo
llo
ws.
Tabl
e
1. T
u
ni
n
g
param
e
t
e
rs o
f
M
P
C
Tuning Para
m
e
te
r
Value
Contr
o
l inter
v
al(
s
econds)
(sa
m
pling interval)
1.
0
Pr
ediction hor
izon
10
Contr
o
l hor
izon
3
Rate weight for
in
puts
0.
1
W
e
ight for
dr
u
m
p
r
esur
e
1
W
e
ight for
power
output
1
W
e
ight for
water
level
deviation
0
Dur
a
tion(
seconds)
30
Robustnes
s
0.
8
3.
CONTROLLER PERFORMANCE
W
i
t
h
th
e tun
i
ng
p
a
ram
e
ters sp
ecified in
tab
l
e 1
and
th
e fo
llo
wi
n
g
setpo
i
n
t
s,
Dr
um
press
u
r
e
:
12
0
k
g
/
s
q
.
c
m
Ou
tpu
t
Po
w
e
r
:
12
0 M
W
W
a
t
e
r
l
e
vel
de
vi
at
i
on:
ze
ro
The set
p
oi
nt
t
r
acki
n
g res
p
on
s
e
s of t
h
e M
P
C
cont
rol
l
e
r i
s
de
pi
ct
ed i
n
fi
g
u
re
2 sh
o
w
i
n
g t
h
a
t
t
h
e per
f
o
r
m
a
nce o
f
M
P
C
i
s
excel
l
e
nt
wi
t
h
ve
ry
go
od
t
r
a
n
si
ent
a
n
d st
ea
dy
st
at
e r
e
sp
onses
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
JECE Vo
l. 5
,
N
o
. 5
,
O
c
tob
e
r
20
15
:
105
4
–
10
61
1
057
Fig
u
r
e
2
.
Set po
in
t r
e
spon
se of
MPC
4.
EFFECT
OF
VA
RI
ATIO
N IN CO
NTR
O
L
HO
RIZ
O
N
In
th
is section
,
th
e co
n
s
equ
e
n
ces of v
a
ryi
n
g
MPC co
n
t
rol h
o
r
izo
n
v
a
lue will b
e
in
v
e
stig
ated
. Th
e
fol
l
o
wi
n
g
pl
ot
s
i
n
Fi
g
u
re 3 a
n
d
4 depi
ct
t
h
e
set
p
o
i
n
t
t
r
ac
ki
n
g
resp
o
n
se of
M
P
C
wi
t
h
c
ont
rol
h
o
ri
zo
n
(C
H)
equal
t
o
1, 2 an
d 5 respect
i
v
el
y
keepi
n
g
p
r
e
d
i
c
t
i
on
h
o
r
i
z
o
n
fi
xe
d
at
10
[
1
2
]
.
Fig
u
r
e
3
.
Set po
in
t r
e
spon
ses w
ith
co
n
t
r
o
l hor
izon
(
C
H
)
v
a
l
u
e o
f
1
and
2
Evaluation Warning : The document was created with Spire.PDF for Python.
IJECE
ISS
N
:
2088-8708
Model Pre
dictive Control
Syst
em
Design f
o
r
Boiler T
u
r
b
ine
Proce
ss
(Sa
ndeep
K
u
ma
r
Sun
o
r
i)
1
058
Fig
u
re 4
.
Set po
in
t respon
ses with
co
n
t
ro
l horizon
(C
H)
v
a
l
u
e o
f
5
Fig
u
re 3
and
4
clearly in
d
i
cate th
at settlin
g
ti
m
e
o
f
respo
n
s
es is v
e
ry l
a
rg
e
with
con
t
ro
l horizon
eq
u
a
l t
o
1
h
e
n
c
e set po
in
t t
r
ack
i
ng
is
wo
rst i
n
th
is case
bu
t
th
e p
e
ak ov
ersh
oo
t in
water l
e
v
e
l respon
se i
s
least.
The
res
p
onses
are alm
o
st sim
i
lar with
co
nt
r
o
l
ho
ri
zo
n
val
u
e
o
f
2 a
n
d
5
.
5.
SIG
N
IFI
C
A
NCE
OF
CO
NTROL
HO
R
I
Z
O
N TO P
R
EDICTI
ON
H
O
RIZ
O
N
R
A
T
IO
The fi
g
u
r
e 5 s
h
o
w
s t
h
e co
nt
r
o
l
l
e
r res
p
o
n
se
wi
t
h
co
nt
r
o
l
h
o
ri
z
on
val
u
e o
f
2 an
d p
r
edi
c
t
i
on h
o
r
i
z
o
n
val
u
e o
f
4
i
.
e
c
ont
rol
t
o
pre
d
i
c
t
i
on ho
ri
zo
n r
a
t
i
o
(M
/
P
)
=0
.
5
Fig
u
re 5
.
Set po
in
t respon
ses with
M/P=0
.
5
No
w t
h
e c
o
m
p
ari
s
on
o
f
fi
g
u
re
4 a
nd
fi
g
u
r
e 5
re
veal
s that th
e sam
e
v
a
lu
e of M/P rat
i
o
resu
lts in
id
en
tical set poin
t
track
i
n
g resp
on
se.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
JECE Vo
l. 5
,
N
o
. 5
,
O
c
tob
e
r
20
15
:
105
4
–
10
61
1
059
6.
EFFECT OF
VARIAT
ION IN RATE WE
IGHT
ON M
ANIPUL
A
TED VARIABLE
MOVES
The fi
gure 6
presents the c
o
m
p
arision of c
a
lculat
ed m
oves in the three
m
a
nipulated
variables wit
h
in
pu
t r
a
te w
e
i
g
h
t
of
0.1
and
0.8
.I
t cl
early ind
i
cates th
at th
e in
crease i
n
the v
a
lu
e
o
f
inp
u
t rate weigh
t
resu
lts
i
n
de
crease
i
n
t
h
e
req
u
i
r
e
d
val
u
e
o
f
di
scr
e
t
e
m
oves i
n
m
a
ni
pul
at
ed
v
a
ri
abl
e
i
.
e
dec
r
ease i
n
t
h
e
a
m
ount
o
f
cont
rol
e
f
f
o
rt
r
e
qui
red
f
o
r
set
poi
nt
t
r
ac
ki
n
g
.
Fi
gu
re
6.
M
a
ni
pul
at
ed
va
ri
abl
e
m
oves wi
t
h
i
n
p
u
t
rat
e
wei
g
ht
o
f
0.
1 a
n
d
0.
8
7.
EFFECT OF
VA
RI
ATIO
N
IN S
A
MPLI
N
G
INTE
RV
A
L
The effect of variation of the sam
p
lin
g
in
terv
al for MPC in
th
e setp
o
i
n
t
track
ing
resp
on
se is rev
ealed
in fi
gu
re
7.
Th
e res
p
o
n
ses
ar
e o
b
taine
d
f
o
r
three
dif
f
eren
t
v
a
lu
es of sam
p
lin
g in
terv
al
wh
ich
are
1
,
2 and
5
seco
nds
. T
h
e s
e
t
t
e
l
i
ng t
i
m
e
s of t
h
ese
res
p
o
n
s
es f
o
r
t
h
e
o
u
t
p
ut
d
r
um
pres
su
re are
o
b
se
rve
d
t
o
be
1.
38
,
2.
0
3
a
n
d
5 sec
o
nds
res
p
ect
i
v
el
y
and
f
o
r
t
h
e
o
u
t
p
ut
po
we
r
out
put
are
0.
99
,
2.
03
an
d
5 sec
o
nd
s res
p
ect
i
v
el
y
,
w
h
i
c
h
clearly sh
ows th
e
d
e
grad
atio
n in
p
e
rform
a
n
ce with
i
n
crease in
th
e v
a
l
u
e
o
f
sam
p
lin
g
in
terv
al.
Fig
u
re
7
.
Set
po
in
t track
i
ng
resp
on
se of MP
C
fo
r sam
p
l
i
n
g
i
n
t
e
r
v
al
o
f
1,
2
an
d
5 sec
o
nds
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
ECE
I
S
SN
:
208
8-8
7
0
8
Mod
e
l Pred
ictive Con
t
ro
l
S
y
st
em
Design
f
o
r
Bo
iler Tu
rb
in
e Pro
c
ess
(Sa
ndeep
K
u
ma
r
Sun
o
r
i)
1
060
8.
CO
NCL
USI
O
N
It h
a
s
b
een
observ
e
d
th
at th
e respon
se of
MPC co
n
t
ro
ller is v
e
ry
fast with
v
e
ry sm
al
l settlin
g
ti
me
and
ve
ry
go
o
d
set
poi
nt
t
r
acki
ng
per
f
o
rm
ance as com
p
ared
t
o
t
h
e co
n
v
ent
i
onal
c
ont
rol
l
e
r
s
. Fu
rt
he
r i
t
ha
s been
f
oun
d th
at t
h
e
sam
e
co
n
t
r
o
l
ho
r
i
zo
n to pr
ed
i
c
tio
n
ho
r
i
z
on ratio
resu
lts in sa
m
e
c
o
n
t
ro
ller
p
e
rf
or
m
a
n
ce an
d th
e
in
crease in
th
e v
a
lu
e of in
pu
t
rate weig
h
t
resu
lts in
d
ecrea
se i
n
cont
rol
m
oves i
n
m
a
nipul
at
ed
vari
a
b
l
e
s.
A
n
in
crease i
n
th
e v
a
lu
e
o
f
sam
p
lin
g
i
n
terv
al for MPC h
a
s resu
lted
in
an
i
n
crease i
n
th
e
settelin
g
ti
m
e
o
f
t
h
e
r
e
spon
se
REFERE
NC
ES
[1]
M. Sarailoo
,
B
.
Reza
ie
and Z
.
Rahm
ani, “
M
L
D
Model
of Boi
l
er Turb
ine S
y
s
t
em
bas
e
d on P
W
A Lineari
zat
i
o
n
approach
”,
International
Journal of Com
puter Science and
Engineering
, 2(4)
,
pp. 8
8
-92, 2012
.
[2]
Junxia Mu and David Rees, “Approximate Model
Predictive Co
ntrol for Gas Turbine Engin
e
s”,
Pr
oceed
ing of th
e
2004 American
Control Confer
ence
Boston, Massachusetts,
2004
.
[3]
T. R
a
jkum
ar,V
.
M
. Ram
a
a
P
r
i
y
a
a
and
K
.
Gobi,
“Boiler Drum Level Control
b
y
using Wide Op
en Contro
l with
Three Elem
ent Control
S
y
s
t
em
”,
International Monthly Re
ferred Journal of Resear
ch in
Management an
d
Technology,
Vol. II
, 2013
.
[4]
Rekha Rajan, M
uhamme
d Salih P and N. Anil Kum
a
r, “
S
peed
Controller Design for Steam Turbine”,
In
ternation
a
l
Journal of Ad
va
nced
Research
in Electrical Elec
tronics and Instr
u
mentation
Engineering
”, Vol. 2
,
Issue 9, 2013.
[5]
Omar Shahin, Mohammad El-Bardini a
nd Nabila M. El-Rab
aie, “Control Sc
heme of a Boiler Turbin
e using
Adaptive Wavelet Neural Network”,
Journal of
Engineering Sc
i
e
nces
,
A
s
s
i
ut Univer
s
ity
,Vol. 39,
No. 6, pp. 1387-
1401 ,2011
.
[6]
Saeed Balo
chia
n and Soheil Vosoughi, “
D
esig
n and Sim
u
lation of Turbine Speed Control S
y
stem
based on
Adaptive
Fuzz
y
PID Controller
”
,
Advan
ces
in Mechanica
l Eng
i
neer
ing and
its Ap
plications (
A
MEA)
,
Vol.
1,
No.
3,
2012.
[7]
Wen Tan and F
a
ng Fang, “Lin
ear Analy
s
is a
nd
Control of a Bo
iler Turb
ine Unit”,
Proceed
ings of the 17
th
Word
Congress the International Fed
e
ration of
Au
tomatic Congress, Seo
u
l, Korea
,
2008
.
[8]
Pradeep Kum
a
r
Juneja, A
.
K. Ra
y,
“
P
redict
ion B
a
sed Control o
f
Lim
e
Kiln Proc
ess in a Paper
Mill”
,
Journal o
f
Forest Products
and Industries
, 2
(
3), pp
. 58-62
, 2
013.
[9]
Pa
ng-c
h
ia
Che
n
, Je
ff S.
Sh
a
mma
, “Gain Scheduled l
1
-Optimal Control for Boiler
Turb
ine D
y
n
a
m
i
cs with Actuato
r
Saturation
”
,
Jou
r
nal of
Process
Control
14(3), p
p
. 263-277
, 200
4.
[10]
Shao
y
u
an
Li, H
ongbo Liu, Wen
-
Jian Cai, Yeng-
Chai Soh a
nd Li-Hua Xie, “A New Coor
dinated Control
Strateg
y
for Boiler Turb
ine S
y
stem of Coal Fired Power
Plant”,
I
E
EE Transactions on Control Systems Technology,
Vol.
13,
No. 6, 2005
[11]
FAN liping, ZHANG
Jun, HUA
NG
Xing, HU
A
NG Dong,
“The Design of th
e MISO
Model Pr
edictive Contro
ller
for Bi
orea
ct
or”,
TELKOMNIKA Indonesian Journ
a
l
of Electrical
Engineering
, Vol. 10
, No
. 6
,
pp. 1
163-1170, 2012
.
[12]
Fateme Pirouzm
a
nd, “Robust M
odel Pred
ic
tive
Control b
a
sed o
n
MRAS for Sa
tellite Attitude
Control s
y
s
t
em”,
International Jo
urnal of
Electr
ical and Computer Engin
eering
, V
o
l. 4
,
No
. 1
,
pp
.
81-92, 2014
.
[13]
Deepa Thang
a
relusamn,
Laksh
mi Ponnusam
y
,
“Elimination
of Ch
attering us
ing
Fuzzy
Sliding Mod
e
Contro
ller
fo
r
Drum
Boiler
Tur
b
ine S
y
s
t
em
”,
C
EAI
, Vol. 15, pp. 78-85, 2013.
BIOGRAP
HI
ES OF
AUTH
ORS
He is working
as
an Asst.
Professor in
ECE
depa
rt
m
e
nt of Graph
i
c
Era Hi
ll
Univers
i
t
y
; Bh
im
tal
Campus. He ear
ned his M.Tech
degree
in Digi
tal Communicatio
n from U.P.T.U, Lucknow and
B.Te
ch d
e
gree
i
n
El
ectron
i
cs
a
nd Com
m
unicat
i
on from
Birl
a
Institute
of App
lied Sc
ien
ces,
Bhim
tal. He has
an experi
ence
of around 12
y
e
ars
of teach
ing and adm
i
nistrat
i
on. He is a life
tim
e m
e
m
b
er of ISTE.His areas
of interest are
m
u
ltivari
a
ble co
ntrol s
y
stem
, di
gital design and
signal pro
cessin
g
.
He has
a P
h
.D fr
om
IIT Roorkee
in the
are
a
of Co
ntrol S
y
s
t
em
s
.
He e
a
rned h
i
s
M
.
T
ech d
e
gree
i
n
Instrumentation
Engineering fro
m University
Ca
mpus, DAVV, Indore and B
.
Tech in Electronics
& Instrum
e
ntati
on from
Institut
e
of
Engg. And
Tech., Univers
i
t
y
Cam
pus, M.J Prohilkhand
Universit
y
,
Bar
e
ill
y.
He
is pr
esen
tl
y working
as
P
r
ofessor in
EEE
Depa
rtment, GEU, Dehradun
.
He has
15
y
ear
s
of teaching a
nd res
earch exp
e
rien
ce
. He has
been the re
cip
i
ent of M
H
RD
scholarship for
two
y
e
ars at II
T
Roorkee dur
ing
Ph.D. and N
e
w I
d
ea Fund scho
larship at C
EERI
Pilani for on
e
y
e
ar during M.Tech. He is a
lif
e m
e
mber of ISTE and annual member of IPPTA.
His research
int
e
rests in
clude
m
u
ltivari
a
ble
co
ntrol s
y
s
t
em
, pr
edic
tion b
a
sed c
ontrol s
y
s
t
em
s,
m
odern control
s
y
s
t
em
,
s
y
s
t
em
th
eor
y
,
fuzz
y
con
t
r
o
l s
y
s
t
em
and
a
proces
s
contro
l.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-87
08
I
J
ECE Vo
l. 5
,
N
o
. 5
,
O
c
tob
e
r
20
15
:
105
4
–
10
61
1
061
She is working
as Professor in
ECE departmen
t
of GEU, Dehra
dun. She has and PhD from IIT
Roorkee. S
h
e ha
s
a vas
t
experien
ce in th
e are
a
of
teach
ing, r
e
s
ear
ch and adm
i
nis
t
r
a
tion
.
S
h
e has
worked as a faculty
member for around 15
y
ear
s
and has taught several
core
co
urses like Basic
Electronics, Electronic Devices
and Circuits,
Communication Engi
nee
r
ing e
t
c. as
wel
l
as
advanc
ed
course
s like
Soft Com
puting Fuz
z
y
Log
i
c Con
t
rol S
y
ste
m
, Artifi
c
ia
l Int
e
lligen
ce
e
t
c.
Evaluation Warning : The document was created with Spire.PDF for Python.