Internati
o
nal
Journal of P
o
wer Elect
roni
cs an
d
Drive
S
y
ste
m
(I
JPE
D
S)
V
o
l.
5, N
o
. 4
,
A
p
r
il
201
5, p
p
.
57
6
~
58
2
I
S
SN
: 208
8-8
6
9
4
5
76
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
/
IJPEDS
Design of Controllers for Co
ntinuous Stirred Tank Reactor
Dr. S. Deep
a,
N. Anipri
ya
, R. Sub
bula
k
s
h
my
Panimalar Institute of
Technolo
g
y
, Chenn
a
i, Ind
i
a
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Dec 1, 2014
Rev
i
sed
Jan 24, 201
5
Accepted
Feb 16, 2014
The objective of
the project is to de
sign various
controllers for temperature
control in Con
tinuous Stirred
Tank
Reactor
(CSTR) sy
st
em
s. Initial
l
y
Zeigl
e
r-Nicho
ls,
m
odified Zeig
ler-Nic
ho
ls, Ty
r
e
us-Lu
y
b
e
n, Sh
en-Yu and
IMC based meth
od of tuned Pro
portional
Integral (PI) controller
is designed
and com
p
aris
on
s
are
m
a
de wi
t
h
F
u
zz
y
Logic Controller
.
Sim
u
lations
are
carried out and
responses are ob
tain
ed
for
the above con
t
rollers. Maximum
peak ov
ershoot,
Settling
tim
e, Ri
se tim
e,
ISE, IA
E ar
e chosen
as
perform
ance
index. From the analy
s
is it is f
ound
that th
e Fuzzy
Logi
c Con
t
roller is a
prom
ising contr
o
ller
than
th
e
co
nvention
a
l
contr
o
llers.
Keyword:
C
ont
r
o
l
l
e
r t
uni
ng
CSTR
Fuzzy logic c
o
ntroller
Propo
rtion
a
l an
d in
tegral
cont
rol
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
:
Dr.
S.
Dee
p
a
,
Pan
i
m
a
lar In
stitu
te of Techn
o
l
o
g
y
,
Ch
enn
a
i ,Ind
ia
Em
a
il: dee_som
s
123@yahoo.co.i
n
1.
INTRODUCTION
Process Indust
ries play a significan
t ro
le in eco
no
m
i
cal
g
r
owth
of
a nat
i
on. Most of the chem
ica
l
process systems are nonlinear in nat
u
re
.
Wh
i
l
e
t
h
ere m
a
y
be an
ext
e
nsi
v
e un
de
rst
a
n
d
i
n
g o
f
t
h
e
be
hav
i
or
o
f
nonlinea
r proc
ess,
satisfact
ory
m
e
thods
for their c
o
ntrol
are
still evolvi
ng.
C
ont
r
o
l
of
t
e
m
p
erat
ure i
s
an i
m
port
a
nt
and
com
m
on
t
a
sk i
n
p
r
oce
s
s i
n
dust
r
i
e
s. F
o
r
exam
pl
e,
con
s
i
d
er t
h
e co
nt
r
o
l
of t
e
m
p
erat
ure i
n
a
boi
l
e
r d
r
um
. Too
hi
gh
or t
oo l
o
w t
e
m
p
erat
ur
e i
n
t
h
e b
o
i
l
e
r d
r
um
can
resu
lt i
n
p
r
ob
le
m
s
. It is im
p
o
r
tan
t
to m
a
in
t
a
in
th
e tem
p
eratu
r
e as close
as po
ssi
b
l
e to th
e
requ
ired
set po
in
t
.
The c
o
ntrol of
te
m
p
erature
in
a CSTR
is a challen
g
i
ng
task
,
th
is is
d
u
e
to
t
h
e
relatio
n
s
h
i
p b
e
tween con
t
ro
lled
vari
a
b
l
e
and t
h
e
m
a
ni
pul
at
ed
vari
a
b
l
e
. The
wi
de ap
pl
i
cat
i
ons
of t
e
m
p
erat
ure co
nt
r
o
l
of
C
S
TR
i
n
cl
ude
s, t
h
e
raw m
a
terials
stock
of t
h
e c
h
em
ical works
with certa
i
n
t
e
m
p
erat
ure
,
a
nd m
i
xi
ng t
h
e
raw m
a
t
e
ri
al
s fo
r
pr
ocess
of t
h
e l
i
t
h
i
f
i
cat
i
on w
o
rks a
nd t
h
e o
u
t
put
pr
od
uct
s
re
act
i
on o
f
t
h
e bi
ochem
i
cal
t
ech
nol
ogy
. M
o
st
o
f
t
h
e
ch
em
ical
in
d
u
stry, o
il/g
as
p
r
od
u
c
tion
i
n
du
stries are
wid
e
ly
u
s
es t
h
e CSTR
for the purpo
se o
f
m
i
x
i
n
g
the two
or m
o
re rea
c
tants at certain t
e
m
p
erature in t
h
e prese
n
ce
of
catalyst to give
s the chem
ical
product
of s
p
e
c
ified
te
m
p
erature
.
There e
x
ists a variety of m
e
thods
f
o
r t
e
m
p
erat
ure c
ont
rol
.
Very
o
f
t
e
n a
PID co
nt
r
o
l
l
e
r i
s
used f
o
r
te
m
p
eratu
r
e co
n
t
ro
l i
n
m
o
st ap
p
lication
.
An conv
en
tion
a
l PID con
t
ro
llers
h
a
v
e
limita
tio
n
in no
n
lin
ear
syste
m
s, co
m
p
lex
an
d
v
a
gu
e th
at h
a
v
e
n
o
p
r
ecise
m
a
th
e
m
a
tical
m
o
d
e
l. To
o
v
e
rco
m
e th
ese d
i
fficu
lties, a class
of n
o
n
co
nve
n
t
i
onal
t
y
pe of
cont
r
o
l
l
e
r em
pl
oy
i
n
g f
u
zzy
l
ogi
c has bee
n
desi
gne
d an
d sim
u
l
a
t
e
d fo
r t
h
i
s
p
u
rp
o
s
e
K
.
S. Tang
(
200
1)
. Fu
zzy lo
g
i
c contr
o
ller
is a p
r
omisin
g
co
n
t
ro
ller
in
ter
m
s o
f
p
e
r
c
en
tag
e
o
v
e
r
s
hoot
and system
re
sponse in temperat
ur
e control in face of
no
nlinea
rities intr
oduced
by pum
ps, valve
s
and
sens
ors
.
Th
e
pu
rp
ose
of
th
e pr
oj
ect is to
d
e
sign
a fuzzy
l
ogi
c c
o
nt
rol
l
e
r
(FLC
)
f
o
r
t
e
m
p
erat
ure
co
nt
r
o
l
.
I
n
recent years
,
fuzzy logic c
o
ntrol has
em
erged as one
of t
h
e princi
ple areas
of
researc
h
in chem
ical p
r
oces
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Desi
g
n
of
C
o
nt
rol
l
e
rs f
o
r
C
o
n
t
i
nuo
us
St
i
rre
d
Ta
nk
React
o
r
(
D
r.
S.
Dee
pa)
57
7
co
n
t
ro
l. Fu
zzy lo
g
i
c con
t
ro
l is esp
ecially su
itab
l
e
for
co
m
p
lex
,
ill-defin
e
d
p
r
o
cesses th
at
d
o
not lend
th
em
selv
es to
co
n
t
ro
l
b
y
co
nv
en
tion
a
l classical co
n
t
ro
l strateg
i
es.
This
pa
per attem
p
ts to use
differe
n
t m
e
thod
of tune
d P
r
oportional
Integral (P
I) controller is de
signe
d
for a tem
p
erature c
ontrol
of CSTR.
Thi
s
pa
per
i
s
o
r
ga
ni
zed as f
o
l
l
o
ws. Sect
i
o
n I
I
pr
esen
ts th
e math
e
m
atica
l
m
o
d
e
lin
g
o
f
CSTR. Section
II
I
prese
n
t
s
t
h
e desi
gn
o
f
Fu
zzy
Lo
gi
c C
o
nt
r
o
l
l
e
r.
Si
m
u
lat
i
on
res
u
l
t
s
a
r
e
di
scuss
e
d
i
n
sect
i
o
n
I
V
.
Fi
nal
l
y
,
sect
i
on
V c
o
nc
l
udes
t
h
e
pa
per
.
2.
CO
NTIN
UO
US STIR
RED
TAN
K
REA
C
TOR
Continuous
Sti
rre
d Ta
nk Rea
c
tor
(CSTR
)
,
also
known as
vat-
or Back
mix reactor, is a c
o
mm
on
type of ideal
re
actor i
n
chem
ical
in
du
stry. C
S
TR is a co
m
p
lex
non
lin
ear
s
y
ste
m
. The Sc
hem
a
tic of a C
S
TR is
sho
w
n i
n
Fi
gu
r
e
1.
Fi
gu
re
1.
The
Schem
a
t
i
c
of a
C
S
TR
2.1.
Mathematical
Model for CST
R
Pr
oce
ss
A si
m
p
l
e
exot
herm
i
c
react
i
on A
→
B takes
place in the
re
actor,
which is
in turn c
oole
d
by a coolant
that flows t
h
rough a
jac
k
et a
r
ound the
react
or.
The
fundam
ental depe
ndent
qu
antities for the reactor a
r
e:
(a)
To
tal m
a
ss o
f
t
h
e
reactin
g m
i
x
t
ure in tank
(b
)
Mass of c
h
em
ical A in the
rea
c
ting m
i
xture
(c)
To
tal en
erg
y
of th
e reacting
mix
t
u
r
e in th
e t
a
n
k
In t
h
i
s
pr
ocess
t
h
e heat
pr
o
d
u
ced
d
u
e t
o
t
h
e react
i
on i
s
r
e
m
oved
by
a cool
a
n
t
m
e
di
um
t
h
at
fl
ows
through a
jac
k
et ar
ound
the reactor.
A
s
k
now
n fr
om
th
e an
alysis o
f
a CSTR
syste
m
, th
e
amount of heat releas
ed
by
t
h
e
ex
ot
he
rm
ic
reaction is a
non linea
r function
of th
e tem
p
erature T
insi
de t
h
e
reactor.
On t
h
e ot
her
h
a
nd
, t
h
e
heat
r
e
m
oved
by
t
h
e
cool
a
n
t
i
s
a l
i
near
fu
nct
i
o
n
of t
h
e t
e
m
p
erat
ure T.
Wh
en
t
h
e C
S
TR
i
s
at
st
eady
st
at
e,
he
at
pr
o
duce
d
by
t
h
e
react
i
o
n
s
h
oul
d
be e
qual
t
o
t
h
e
heat
rem
ove
d
by
t
h
e
co
ol
ant
Let u
s
app
l
y t
h
e co
nserv
a
tion
p
r
i
n
cip
l
e
o
n
th
e three
fund
amen
tal q
u
a
n
tities:
To
ta
l Ma
ss
Ba
la
n
c
e:
(1
)
Mass Bal
ance
on
C
o
mp
o
n
e
n
t
A
:
(2
)
To
ta
l En
erg
y
Ba
lan
ce:
P
K
U
E
(3
)
Ass
u
m
e
the reactor does
not
m
ove (i.e., dK/dt = dP
/
d
t = 0), t
h
e left-ha
nd si
de of the
total energy
balance yields:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l. 5
,
No
. 4
,
Ap
r
il 2
015
:
57
6
–
58
2
57
8
(4
)
Si
nce t
h
e
sy
st
em
i
s
a l
i
qui
d
sy
st
em
, we ca
n
m
a
ke t
h
e
fol
l
o
wi
n
g
a
p
p
r
oxi
m
a
t
i
on:
dt
dH
dt
dU
(5
)
Ch
a
r
a
c
terize To
ta
l Ma
ss
:
(
6
)
Char
acterize t
h
e M
a
ss
of A
:
(
7
)
St
at
e v
a
ri
a
b
l
e
s
:
(8
)
St
at
e E
q
u
a
t
i
on
s:
(9
)
(1
0)
(1
1)
The T
r
a
n
sfe
r
F
unct
i
o
n M
o
del
of
t
h
e C
S
TR
i
s
gi
ve
n a
s
:
(1
2)
3
.
FUZZ
Y LOGIC
CONTROLLER DE
SIGN
Fuzzy logic control is deri
ved from
fuzzy set theo
ry in
troduced
b
y
Lo
fti Zad
e
h
i
n
196
5.
Fu
zzy log
i
c
i
s
a paradi
gm
f
o
r a
n
al
t
e
rnat
i
v
e desi
g
n
m
e
t
h
o
dol
ogy
,
whi
c
h
can be a
ppl
i
e
d
i
n
devel
o
pi
n
g
bot
h l
i
n
ear an
d
no
n-
lin
ear syste
m
s. It is realized
t
h
at in
corpo
r
ati
n
g
h
u
m
an
in
tellig
en
ce in
to
auto
m
a
tic co
n
t
ro
l
syste
m
wo
u
l
d b
e
a
m
o
re efficien
t so
lu
tion
and
this led
to
th
e d
e
v
e
lop
m
en
t o
f
th
e fu
zzy co
n
t
ro
l alg
o
rith
m
s
.
Fu
zzy con
t
ro
l, wh
ich
has i
t
s
root
s i
n
fuzzy
set
dev
e
l
opm
ent
pro
p
o
se
d by
pr
ofe
s
sor Za
deh
,
al
l
o
ws t
h
e e
xpe
ri
ence an
d k
n
o
w
l
e
d
g
e
gai
n
e
d
i
n
p
r
e
v
i
o
us sy
st
em
s fo
r t
h
e c
ont
r
o
l
o
f
pr
oce
sse
s. Fuzzy logic
control is es
pecially suitable for
co
m
p
en
satin
g
n
on-lin
earities. Th
e
syste
m
ati
c
p
r
op
erty
o
f
fu
zzy log
i
c can con
v
e
rt th
e li
n
g
u
i
stic con
t
rol ru
les
base
d on e
x
p
e
rt
kn
owl
e
d
g
e
i
n
t
o
aut
o
m
a
ti
c cont
r
o
l
st
rat
e
gi
es. Suc
h
no
n-l
i
n
ea
r m
a
t
h
em
at
i
c
al
cont
rol
algorithm
s
can be im
ple
m
ented easily in the com
puter.
Th
ey ar
e str
a
ightf
o
rw
ar
d
an
d
sh
ou
ld
no
t in
volv
e
in
any
com
put
at
i
onal
p
r
o
b
l
e
m
s
.
3.
1. Fuz
z
i
fi
cati
on
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Desi
g
n
of
C
o
nt
rol
l
e
rs f
o
r
C
o
n
t
i
nuo
us
St
i
rre
d
Ta
nk
React
o
r
(
D
r.
S.
Dee
pa)
57
9
Fu
zzy log
i
c uses lin
gu
istic v
a
riab
les in
stead
of
n
u
m
e
ri
c vari
a
b
l
e
s. Th
e pr
ocess
of c
o
n
v
e
r
t
i
ng a
n
u
m
erical v
a
ri
ab
le in
t
o
a ling
u
i
stic
v
a
riab
le is called
Fuz
z
ification. In
t
h
e prese
n
t
work
th
e error and ch
an
g
e
in errors a
r
e ta
ken as t
h
e inputs a
nd t
h
e
output. T
h
e e
rro
r
o
f
ran
g
e
is co
nv
erted
in to
sev
e
n lin
gu
istic
v
a
lu
es
nam
e
ly
NB
, NM
, NS
, ZR
,
PS, PM
, PB
.
Sim
i
l
a
rl
y
change i
n
e
r
r
o
r
o
f
som
e
range
i
s
con
v
ert
e
d t
o
seve
n
l
i
ngui
st
i
c
val
u
es. The co
nt
r
o
l
l
e
r out
p
u
t
of
som
e
range i
s
al
so con
v
e
r
t
e
d
i
n
t
o
l
i
ngui
st
i
c
val
u
es nam
e
l
y
NB
,
NM, N
S
, ZR,
PS, PM a
nd P
B
. Triangula
r
me
m
b
ership
function is select
ed an
d the ele
m
ent of the ea
ch of the
term
sets are map
p
e
d
on
to th
e do
m
a
in
of c
o
r
r
esp
o
ndi
ng
l
i
n
gui
st
i
c
vari
a
b
l
e
s.
3.
2. Rul
e
B
a
se
Basically, th
e d
ecision
log
i
c
stag
e is similar to
a ru
le b
a
se
co
nsists th
e fuzzy co
n
t
ro
l ru
l
e
s to
d
eci
d
e
how FLC works.
The
stage is
constr
uct
e
d
by
ex
pe
rt
kn
o
w
l
e
d
g
e a
n
d e
x
peri
e
n
ces.
The
r
u
l
e
s ar
e
gen
e
rat
e
d
heu
r
i
s
t
i
cal
l
y
fr
om
t
h
e resp
o
n
s
e
of
t
h
e c
o
nv
e
n
t
i
onal
c
o
nt
r
o
l
l
e
r.
49
r
u
l
e
s de
ri
ve
d f
r
o
m
careful
a
n
al
y
s
i
s
o
f
t
r
e
n
d
obt
ai
ne
d f
r
om
t
h
e sim
u
l
a
t
i
on of c
o
n
v
e
n
t
i
ona
l
cont
r
o
l
l
e
r an
d k
n
o
w
n p
r
oce
ss kn
o
w
l
e
d
g
e.
The deci
si
on
m
a
ki
ng
stage
processes
the input data
and
com
put
es t
h
e c
ont
rol
l
e
r
o
u
t
p
ut
s.
Tabl
e 1.
R
u
l
e
Tabl
e of Fuzzy
Lo
gi
c
C
o
nt
r
o
l
l
er
de
e
NB NM
NS
ZR
PS
PM
PB
NB
NB NB NB NM
NS
NS
ZR
NM
NS NB
NM
NS NS ZR PM
NS
NB
NM
NS NS ZR PS
PM
ZR
NM
NM
NS
ZR
PS
PM
PB
PS
NM
NS ZR PS
PS
PM
PB
PM
NS ZR PS
PS
PM
PB
PB
PB
ZR
PS PS PM
PB
PB
PB
Figure
2. Surfa
ce View of
Fuz
z
y Rules
3.
3. De
fuz
z
i
ficati
on
The o
u
t
p
ut
o
f
t
h
e r
u
l
e
base i
s
con
v
e
r
t
e
d i
n
t
o
cri
s
p
val
u
e, t
h
i
s
t
a
sk i
s
do
ne
by
def
u
zzi
fi
cat
i
on m
odul
e.
C
e
nt
roi
d
m
e
t
hod
o
f
de
fuzzi
fi
cat
i
on i
s
c
o
nsi
d
e
r
ed
f
o
r t
h
i
s
ap
pl
i
cat
i
on. T
h
i
s
i
s
t
h
e
pr
ocess
w
h
er
e t
h
e
me
m
b
ersh
ip
fun
c
tio
ns are sam
p
led
to
find the grade
of m
e
m
b
ership. T
h
e
n
th
e grade
of
me
m
b
ership is use
d
in
t
h
e fuzzy
l
o
gi
c equat
i
o
ns a
nd
out
c
o
m
e
r
e
gi
o
n
i
s
defi
n
e
d. Th
e fu
nct
i
on
of de
f
u
zzi
f
i
cat
i
on
m
odul
e i
s
t
o
per
f
o
r
m
t
h
e de
fuzzi
fi
cat
i
o
n,
whi
c
h c
o
n
v
e
r
t
s
t
h
e set
of
m
odi
fi
ed
out
put
val
u
es i
n
t
o
a cri
s
p
val
u
e.
Table
2. C
ont
r
o
ller
Gain
Para
m
e
ters fo
r Te
m
p
erature Co
n
t
rol
PI Controller
tun
i
ng
m
e
thod
K
p
T
i
Z
i
egler
-
Nichols 1.
87
1.
60
M
odified Z
i
egler
-
Nichols
1.
71
1.
54
T
y
r
e
us-
L
uy
ben
1.
29
1.
45
Shen-
Y
u 1.
38
1.
56
I
M
C based PI
1.
46
1.
32
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l. 5
,
No
. 4
,
Ap
r
il 2
015
:
57
6
–
58
2
58
0
4. RES
U
LTS AN
D DIS
C
US
SION
Th
e sim
u
lat
i
o
n
resu
lts are giv
e
n
in
th
is chap
ter.
T
h
e set
poi
nt
t
r
acki
n
g
resp
o
n
ses o
f
C
S
TR
usi
n
g
con
v
e
n
t
i
onal
P
I
cont
rol
l
e
r a
n
d Fuzzy
l
o
gi
c cont
rol
l
e
r ha
ve
been sh
o
w
n
.
The res
u
l
t
s
of t
h
e ZN/
M
Z
N
, Ty
reus
-
Luy
b
e
n
,
S
h
en
-
Y
u
an
d
IM
C
B
a
sed t
une
d
PI
a
n
d Fuzzy L
ogi
c Controller are com
p
ared.
Fig
u
re
3
.
Set
po
in
t track
i
ng
of CSTR pro
c
ess with
ZN
PI
con
t
ro
ller
Fig
u
re
4
.
Set
po
in
t track
i
ng
of CSTR pro
c
ess with
MZN
PI
con
t
ro
ller
Fig
u
re
5
.
Set
po
in
t track
i
ng
of CSTR
pro
cess with
Sh
en
-Yu tun
i
ng
PI con
t
ro
ller
Fig
u
re
6
.
Set
po
in
t track
i
ng
of CSTR pro
c
ess with
Ty
reus
-L
uy
be
n t
u
ni
n
g
PI
co
n
t
rol
l
e
r
Fig
u
re
7
.
Set
po
in
t track
i
ng
of CSTR
pro
cess wit
h
I
M
C Based tuned
PI
con
t
r
o
ller
Fi
gu
re
8.
C
o
m
p
ari
s
on
o
f
Z
N
and
M
Z
N
t
u
ne
d P
I
cont
rol
l
e
r
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Desi
g
n
of
C
o
nt
rol
l
e
rs f
o
r
C
o
n
t
i
nuo
us
St
i
rre
d
Ta
nk
React
o
r
(
D
r.
S.
Dee
pa)
58
1
Fi
gu
re
9.
F
u
zz
y
Lo
gi
c C
o
nt
r
o
l
l
e
r fo
r C
S
TR
Fi
gu
re
1
0
. C
o
m
p
ari
s
on
of
Z
N
, M
Z
N a
n
d F
u
zzy
l
ogi
c c
ont
rol
l
e
r
Tabl
e 3.
C
o
m
p
ari
s
o
n
of
Tra
n
s
i
ent
Response
Characteristics
of CSTR
Cont
roller peak
overshoot
(M
p
) in
%
Ts in sec
Tr in se
c
Z
i
egler
-
Nichols 18.
9
8.
2
0.
98
M
odified Z
i
egler
-
Nichols
16.
2
6.
8
1.
7
T
y
r
e
us-
L
uy
ben
14.
3
7.
2
1.
5
Shen-
Y
u 13.
7
6.
9
1.
4
I
M
C based PI
13.
2
6.
3
1.
2
FL
C
-
5.
8
3.
2
Tabl
e 4.
Q
u
ant
i
t
a
t
i
v
e C
o
m
p
ari
s
on
usi
n
g
Pe
rf
orm
a
nce I
ndi
c
e
s
Controller
ISE
IAE ITAE
Z
i
egler
-
Nichols 1.
593
1.
981
10.
52
M
odified Z
i
egler
-
Nichols
1.
334
1.
664
6.
561
T
y
r
e
us-
L
uy
ben
1.
204
1.
543
5.
754
Shen-
Y
u 1.
215
1.
432
5.
642
I
M
C based PI
1.
113
1.
345
5.
861
FL
C
0.
945
1.
367
4.
382
Fi
gu
res
fr
om
Fi
gu
re
3 t
o
1
0
sh
ow
s t
h
e
si
m
u
l
a
t
i
on res
u
l
t
s
o
f
Z
N
, M
Z
N
,
Ty
re
us-
L
uy
b
e
n,
She
n
-
Y
u a
n
d
IM
C
base
d t
u
ne
d P
I
C
o
nt
r
o
l
l
e
r r
e
spo
n
se
f
o
r
C
S
TR
sy
st
em
i
s
sho
w
n.
As see
n
fr
om
t
h
e sim
u
l
a
t
i
on res
u
l
t
s
,
PI-
Z
N
Acc
o
u
n
t
fo
r t
h
e l
a
rge
s
t
am
ount
of
o
v
e
r
sh
o
o
t
,
but
has
a fast
resp
o
n
s
e
. PI
-M
Z
N
, T
y
reus
-Luy
ben
,
She
n
-
Y
u
ha
s a
rel
a
t
i
v
el
y
l
e
ss
perce
n
t
a
ge
ove
rsh
o
o
t
whe
r
eas
t
h
e fuzzy
l
o
gi
c cont
r
o
l
l
e
r res
u
lts in
n
o
ov
ersh
oo
t. Th
e MATLAB si
m
u
latio
n
of PI-ZN,
PI-MZ
N
, PI-T
yreus
-
Luy
b
en,
She
n
-Yu a
n
d Fu
zzy logic c
o
ntroller. T
h
us we see there is a
tradeoff
betwe
e
n pe
rce
n
tage
ove
rs
ho
ot
an
d sy
st
em
respons
e. From
t
h
ese resul
t
s
i
t
i
s
agai
n sai
d
t
h
at
Fuz
z
y
l
ogi
c cont
r
o
l
l
e
r i
s
a prom
i
s
i
ng co
nt
r
o
l
l
e
r
in tem
p
erature
cont
rol i
n
re
actor.
5. CO
N
C
L
U
S
I
ON
This
pa
per
pres
ents te
m
p
erature cont
rol
in Continuous
Stirre
d Ta
nk React
or
[1] Fuzzy logi
c
co
n
t
ro
l is especially su
itab
l
e for co
m
p
lex
syste
m
s.
The fuzzy
base
d
cont
rol
l
e
rs a
r
e
usef
ul
w
h
en
preci
s
e
math
e
m
atica
l
form
u
l
atio
n
s
are infeasib
le.
In
con
v
e
n
tional PI co
n
t
ro
ll
ers tun
i
ng
m
e
th
od
s
h
a
s m
o
d
e
rate
transient
res
p
onse c
h
aracteris
tics a
nd
per
f
o
r
m
a
nce i
nde
x.
Fuzzy
l
o
gi
c co
nt
r
o
l
l
e
r has t
h
e bet
t
e
r res
p
on
se a
n
d
per
f
o
r
m
a
nce indi
ces t
h
a
n
co
nve
nt
i
o
nal
PI
cont
rol
l
e
r t
u
ni
ng m
e
t
hod.
Fr
om
t
h
e anal
y
s
i
s
i
t
can be co
ncl
u
ded
that Fuzzy
Logic Controller is
a prom
is
i
ng c
ont
rol
l
e
r i
n
pr
o
cess i
n
du
st
ri
es.
REFERE
NC
ES
[1]
Deng Xiaosong, D Popovie, G
Schulz-Ek
loff.
Real
time id
en
tification an
d con
t
rol of
a contin
uous stirred tan
k
reactor with neu
r
al n
e
twork.
IEEE Trans.
1995.
[2]
T Tak
a
gi, M Su
geno.
Fuzzy id
entifi
cation o
f
systems and its applicat
ions to modeling and con
t
rol.
IE
EE Tr
ans,
On
SMC, 1985.
[3]
Robert Babuska, HB Verbruggen: An ove
rview
of Fuzzy
Mod
e
lling for
contr
o
l,
Control Eng
i
neering Pra
c
tice.
1996.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l. 5
,
No
. 4
,
Ap
r
il 2
015
:
57
6
–
58
2
58
2
[4]
George Steph
a
n
opoulos. Chemical Process Contr
o
l. Pr
en
tice H
a
ll
of India Pvt. Ltd
.
, New Delh
i, 20
01.
[5]
R. Suja Man
i
M
a
lar
,
T Th
y
a
g
a
rajan. Artificial N
e
ural
Networks
Based Modeling
and con
t
rol of
Continuous Stirr
e
d
Tank R
e
actor.
American Journal of
Engine
ering
and Applied s
c
ience.
2009.
[6]
Q Wu,
Yj Wang,
QM
Zhu, K
Warwick.
N
e
urofuzzy model ba
sed predictive
control of non
lin
ear CSTR system
.
Proceeding
of
th
e IE
EE
int
e
rnat
i
on. 2002
.
[7]
U Sabura Banu,
G Uma.
Modelling of CSTR by Fuzzy Clustering
.
P
r
oceedings
of I
ndia Int
e
rnat
ion
a
l Confer
enc
e
o
n
Power Electron
ics, 2006
.
[8]
Tiejun
Zh
ang. Fuzzy
Anti-Wind
up d
y
namic out
p
u
t feedback
control of non
lin
ear
process.
I
EEE
T
r
ans
.,
2009.
[9]
Timoth
y
J Ross.
Fuzzy
log
i
c with
Engin
eering
Ap
plications. McGr
aw Hill
, INC, N
e
w delh
i. 2002.
[10]
Youen Zhao
, S
houjun Zhou
,
Li Li.
D
y
n
a
mic Characteristics Modeling
of
a
CSTR Using Neural Network.
First
International
Co
nference on
Intel
ligent
Networks
and Intellig
ent
S
y
stems.
2008
.
Evaluation Warning : The document was created with Spire.PDF for Python.