TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 14, No. 2, May 2015, pp. 298 ~ 30
3
DOI: 10.115
9
1
/telkomni
ka.
v
14i2.766
1
298
Re
cei
v
ed Fe
brua
ry 18, 20
15; Re
vised
April 22, 201
5; Acce
pted
May 1, 201
5
Resear
ch on a Kind of PLC Based Fuzzy-PID Controller
with Adjustable Factor
Wei Xie
*
1,2
, Jianmin Duan
1
1
Beijin
g Ke
y L
a
borator
y of T
r
affic Engine
erin
g, Beiji
ng Un
iv
ersit
y
of T
e
chn
o
lo
g
y
, Be
iji
ng 1
001
24, Ch
ina
2
Beijin
g Pol
y
te
chnic, Bei
jin
g 1
001
76, Ch
ina
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: xie
w
_
b
j
@
hot
mail.com
A
b
st
r
a
ct
A kind of fu
zz
y
-
PID controll
er w
i
th adjustab
l
e
factor
is desig
ned i
n
this pa
p
e
r. Scale facto
r
’
s
se
lf-
adj
ust w
ill co
me true. Fu
zz
y
c
ontrol
alg
o
rith
m is fi
ni
s
hed
in
STEP7 softw
are, and t
hen
d
o
w
n
loa
d
e
d
in
S7-
300 PL
C. W
i
n
CC softw
are w
ill be use
d
to control th
e
chan
ge-tre
nd i
n
real ti
me. D
a
ta co
mmun
ic
atio
n
betw
een S
7
-30
0
PLC
an
d W
i
nCC is
ach
i
ev
ed by MPI. T
h
e rese
arch s
h
o
w
s that this fuzz
y
-
PID co
ntro
lle
r
has better rob
u
st capab
ility a
nd stabi
lity. It
’
s
an effe
ctive method i
n
contro
llin
g co
mp
lex l
ong ti
me-v
aryi
n
g
delay system
s.
Ke
y
w
ords
: fu
z
z
y
-PID, adj
usta
ble factor, temperatur
e contro
l, MPI
Copy
right
©
2015 In
stitu
t
e o
f
Ad
van
ced
En
g
i
n
eerin
g and
Scien
ce. All
rig
h
t
s reser
ve
d
.
1. Introduc
tion
Tempe
r
atu
r
e
control i
s
very importa
n
t
in industria
l produ
ction.
The most comm
on
temperature
control obj
ect
s
in mo
der
n i
ndu
stry are b
o
iler, ele
c
tri
c
fu
rna
c
e, the
control
system
of
steam
plant
and di
still
ation colum
n
[1].
Tempe
r
ature contro
l syst
em
gen
erally ha
s th
e
cha
r
a
c
teri
stic of larg
e ine
r
tia and
delay,
so it’s
di
fficult
to establi
s
h
mathemati
c
al
model
exactl
y.
In indu
strial
prod
uctio
n
p
r
oce
s
s, so
me
c
ont
rol met
hod
s have
b
een em
ploye
d
, su
ch a
s
PID
control [2], Smith pre
d
icti
ve cont
rol [3
], Model
p
r
e
d
ictive control, Fuzzy co
ntrol [4], Ro
bust
control [5]
Neural
net
work [6]. PID controller is
still
widely
used i
n
process
control field for its
many advant
age
s. But for the time
-varying p
r
o
c
e
ss
with la
rg
e time-d
elay, traditional
PID
algorith
m
ha
s many sh
ortcoming
s
: the control accu
racy is lo
w, the stru
cture is difficul
t
to
stabili
ze and
the algorith
m
is more sensitive
in the match de
g
r
ee of the model
s. Theref
ore,
industrial process
control
whi
c
h has l
a
rge ti
me-delay is still a
recogn
ized diffi
cult problem
at
pre
s
ent.
An
d for
la
rge
lag, time-varying
pro
c
e
s
s
who
s
e obje
c
t
pa
rameters ch
an
ged as
wo
rki
n
g
con
d
ition an
d
environm
ent cha
nge
d, it is more difficult
to control it.
Fuzzy
con
t
rol ha
s
the
ch
ara
c
te
risti
c
th
at d
o
e
s
n’t charged
with the
obj
e
c
t mo
del
and
with
stro
ng robu
st, bu
t conventio
na
l fuzzy
c
ont
rol
ca
n n
o
t overcome
n
egative effect
s
cau
s
e
d
by large-la
g very well. In this page we
’ll give
a desi
gn of a hybrid fuzzy controlle
r.
2. The Select and Implement of
Con
t
r
o
l Method
Comm
only u
s
ed t
w
o
-
dime
nsio
nal fu
zzy
cont
rol
sy
st
e
m
alw
a
y
s
t
a
k
e
s
sy
st
em
at
ic
err
o
r
e
and the erro
r rate ec a
s
input variabl
e
s
. This
ki
nd
of control
system ca
n be
divided into two
c
a
tegor
i
es
: f
u
zz
y PD
c
o
ntr
o
l and fuzzy PI c
ontr
o
l.
Fuzz
y PD
c
o
ntr
o
l tak
e
s
u as
output
while
f
u
zzy
P
I
co
nt
rol t
a
ke
s
∆
u
as
output
[7]. In thi
s
p
age,
we
choo
se
f
u
zzy PI controller as
sho
w
n in
Figure 1 [8].
Figure 1. Fuzzy PI control’
s blo
ck di
agram
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Re
sea
r
ch on
a Kind of PLC Base
d Fu
zzy-PID C
ontroller with Adj
u
stabl
e Facto
r
(Wei Xie)
299
i
i
Z
In this
fuzz
y c
ontroller,
u
t
is co
ntrol variable,
Z
is controlled variable,
SV
is referen
c
e
input, the input of fuzzy co
ntrolle
r is error
E
t
and error differen
c
e
EC
t
, the output is
∆
U
t
.
K
e
and
K
c
are th
e qu
antify factors
of erro
r
and
e
rro
r
rate
re
sp
ectiv
e
ly
.
K
u
is the p
r
opo
rtio
n facto
r
of fu
zzy
PI controll
er.
The fu
zzy
control alg
o
ri
thm ha
s be
en b
r
oug
ht i
n
to effect in
Step7 [9] a
nd
download
ed
i
n
S7-300PL
C
, the mo
nitor pictu
r
e
an
d t
ende
ncy
ch
art have b
een
establi
s
h
ed
by
monitor
software
WinCC [1
0] and used to monitor
the
cha
nge tre
n
d
s
of cont
rolle
d plant, the data
comm
uni
cati
on bet
wee
n
S7-30
0
PL
C
and
Win
CC i
s
built
by MPI net. In this
page
we
ch
o
o
se
AE2000A pro
c
e
ss
cont
rol equipm
ent’s
boile
r tem
perature a
s
co
ntrolled plant.
The fu
zzy
co
ntrol al
go
rith
m wa
s
re
alized by
i
nqui
ri
ng a
two
-
dim
ensi
onal t
abl
e on
-line.
The process
can b
e
divide
d into the followin
g
three
steps:
Step 1: Calcu
l
ate system'
s
error a
nd error
rate a
c
co
rding to the sa
mpling si
gnal
and the
given value in the control circuit. Then
fuzzed t
he error an
d error rate accordi
ng to these two
equatio
ns:
K
e
= n
/
e
ma
x
and
K
c
=m
/
c
max
Step 2: Inquire the two
-
dim
ensi
onal ta
ble acco
rdin
g to the fuzzifie
d error and
e
rro
r rate.
In Step7, there’s no
spe
c
ia
l instru
ction f
o
r inqui
rin
g
two-dime
nsio
nal table. As
we kno
w
that the
data st
ru
cture
in microp
ro
cessor is li
nea
r, so
we
writt
en a two-dim
ensi
onal
polli
ng routine b
a
s
ed
on thi
s
cha
r
a
c
teri
stic. In th
e two
-
dim
e
n
s
ion a
r
ray whi
c
h
ha
s
n
×
m
factors, the
ph
ysical
ad
dre
s
s of
cell data
α
[
i
][
j
]
i
s:
(f
irst
a
d
d
r
es
s +
i
×
n
+
j
). Acco
rdin
g to the ab
sol
u
te physi
cal
ad
dre
ss
and St
ep7
STL instructio
ns’ characte
ri
stic,
we
can g
e
t the value of cell data
α
[i][j]
.
Step 3: In order to
co
ntro
l the co
ntroll
ed pla
n
t we
sho
u
ld d
e
fuzzy the fuzzy
cont
rol
variable
∆
u
which
we got from step 2. Th
e defuzzification equ
ation i
s
:
K
u
=
∆
u
ma
x
/h.
3. The Desig
n
of Self-a
djusting Fu
zz
y
Contr
o
ller
The fuzzy co
ntrolle
r is co
mposed of the followin
g
four elem
ents:
1. A rule-b
ase (a set of If-Then rule
s),
whi
c
h
contain
s
a fuzzy logi
c qua
ntificati
on of the
expert'
s lingui
stic de
scriptio
n of how to a
c
hieve g
ood
control.
2. An infere
nce
me
chani
sm (also call
ed an
"i
nference en
gine"
or "fuzzy in
feren
c
e"
module
)
, whi
c
h emul
ates
the expert'
s deci
s
io
n making in interp
reting and a
p
plying kn
owle
dge
about ho
w be
st to control the plant.
3. A fuzzifi
ca
tion interfa
c
e
,
which conv
erts
cont
rolle
r input
s into
informatio
n that the
inferen
c
e m
e
cha
n
ism
can
easily u
s
e to activate and
apply rule
s.
4. A defuzzification inte
rface, which
conve
r
ts t
he con
c
lusi
o
n
s of the i
n
feren
c
e
mech
ani
sm i
n
to actual in
p
u
ts for the proce
s
s.
Th
e fuzz
y co
ntr
o
l rule is
: IF
E
=
A
i
THE
N
IF
EC
=
B
j
THEN
∆
u=C
ij
,
whi
c
h
can
be
desc
r
i
b
ed by
th
e
f
u
zzy r
e
latio
n
s
h
i
p
R
1
that
is
R =
∏
A
B C
,
w
h
e
n
t
h
e e
r
r
o
r
a
n
d e
r
ro
r rat
e
a
r
e
t
a
ken from th
e fuzzy subset A and B separ
ately, we
can g
e
t the output va
r
i
ab
l
e
∆
u
= (A
×B
○
R
1
)
th
ro
u
g
h
fuzzy
de
ducti
on
rul
e
s. The “ce
n
ter of m
a
ss” d
e
fuzzifi
cation
(Sun Ze
n
gqi
etc.,
2
0
0
4
)
i
s
:
)
(
/
)
(
1
0
t
c
t
t
M
t
c
z
z
z
Z
We
can get
a que
ry
ta
bl
e
from th
e
fuzzy cont
roll
er whi
c
h
we
desig
n
e
d
in
M
A
TL
AB
’s
f
u
zzy
to
olb
o
x
[
1
1
]
, as sh
own
i
n
Tab
l
e
1.
Table 1. The
Query Ta
ble
of Fuzzy PID Controlle
r’s
Control Vari
abl
e
∆
u
∆
u
Error
ra
te
ec
-
6
-
5
-
4
-
3
-
2
-
1
0
1
2
3
4
5
6
E r r
o r
e
-6
-5.8
-5.5
-5.4
5
-5.5
-5.4
8
-
5.0
9
-4
.2
2
-
3
.
7
3
-2
.8
-2
.3
3
-
1
9
.
-1
.0
0
0
.
3
5
-5 -5.
5
-5.
5
9
-5.
4
6
-5.
5
9
-
5.
5
-
5.
0
9
-
4
.
2
-3.
6
-2.
6
7
-
2.
2
9
-1.
6
1
0.
12
0.
61
-4 -5.
4
5
-5.
4
6
-5.
2
2
-5.
1
9
-
5.
1
7
-
4
.
2
6
-
4.
2
5
-
3
.
0
2
-
2.
3
3
-
1
.
1
0.
15
1.
06
1.
19
-3 -5.
5
-5.
5
9
-5.
1
9
-5.
0
9
-
4.
8
8
-
3
.
6
8
-
3.
1
1
-
2
.
2
6
-
1.
3
9
0.
30
1.
1
2.
15
2.
33
-2 -5.
4
8
-5.
5
-5.
1
7
-4.
8
8
-
4.
7
2
-
3
.
4
7
-
2.
7
-
1.
9
6
0.
18
0.
15
1.
19
2.
32
2.
8
-1 -5.
0
9
-5.
0
9
-4.
4
3
-3.
6
8
-
3.
4
7
-
2
.
2
8
-
1.
0
9
0.
30
0.
97
1.
29
2.
79
3.
52
3.
73
0
-
4
.
3
3
-
4
.
1
4
-
2
.
8
3
-
2
.
2
4
-
2
.
0
0
-
1
.
1
3
0
1
.
13
1
.
89
2
.
24
3
.
43
4
.
14
4
.
33
1
-
3
7
3
-
3
5
2
-
1
5
7
-
2
0
9
-
0
9
3
0
3
1
16
3
03
4
00
4
11
4
15
5
09
5
09
2 -2
.8
-2
.3
3
-1
.1
8
-0
.3
0
0
.5
6
1
.5
3
2
.2
3
3
.0
5
4
.2
0
4
.2
2
5
.1
7
5
.
5
5
.
4
8
3 -2
.3
3
-2
.1
-0
.8
0
.
3
1
.3
9
2
.3
4
3
.1
1
3
.1
1
4
.2
2
4
.8
4
5
.1
9
5
.
5
9
5
.
5
4
-1.
1
9
-0.
8
0
0.
3
1.
56
1.
74
3.
02
3.
25
4.
26
4.
47
5.
15
5.
22
5.
46
5.
45
5
-0.
7
8
0.
12
1.
1
2.
19
2.
70
3.
48
4.
14
4.
92
4.
92
5.
5
5
.
46
5.
59
5.
5
6
0.
45
0.
78
1.
9
2.
37
2.
8
3
.
7
3
4
.
2
2
5
.
0
9
5
.
4
8
5
.
5
5.
45
5.
5
5.
8
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 2, May 2015 : 298 – 303
300
3.1. The Mod
i
fication o
f
Tempera
t
ure
Fuzz
y
Controller’s Quer
y
Table
Becau
s
e
of the particul
a
rity of experim
e
n
t inst
allation, we
need to a
d
just the
temperature
fuzzy
cont
roll
er’s qu
ery ta
ble. T
he
boil
e
r’s ele
c
tri
c
heating
sil
k
i
s
three-pha
se
resi
stan
ce
wi
re and the three
-
p
h
a
s
e e
l
ectri
c
heatin
g tube's
current is
control
l
ed by SCR’
s
con
d
u
c
tive a
ngle. Th
rou
g
h
expe
riment
we
kn
ew th
at whe
n
the
max value of
PLC’
s an
alo
g
output mod
u
le wa
s 276
48,
the value of electri
c
he
ating tube’
s am
meter was 4.
2A. There’
s n
o
curre
n
t di
spla
y until the val
ue of P
L
C’
s
analo
g
o
u
tpu
t
module
wa
s abo
ut 12
500
and
then
th
e
resi
stan
ce
wi
re sta
r
ted h
e
a
ting. At the begin
n
ing
of
the test, the tempe
r
ature value was
risi
ng
and the
fuzzy
co
ntrolle
r’s q
uery valu
e
was flo
a
ting b
e
t
ween
[6,-6] a
nd [6,6], that’
s
ju
st the
dat
a
in the last line of Table 1. If the inquire
d val
ue is too
small, the quantified outp
u
t value will be
very little an
d the t
r
an
sf
erred
anal
og
output
valu
e will
be
to
o little to
re
ach
the S
C
R’s
con
d
u
c
tive value, so the
SCR ca
n’t be co
ndu
cte
d
and the resi
stan
ce wi
re can’t work.
Acco
rdi
ng to
analysi
s
b
a
se
d on
control
theory,
we
kn
ow th
at large
r
control effe
ct is ne
eded
i
n
the ri
sing
sta
ge
so
as to
make
a
c
tual
value rea
c
h
set valu
e
rap
i
dly. So, we
j
u
st m
odified
the
last line of Ta
ble 1, the ne
w modified q
uery table a
s
sho
w
n in Ta
b
l
e 2.
Table 2. The
Modified Qu
e
r
y Table of
∆
u
Error
e
Error Ra
te
ec
3.
0
3.
0
3.
5
3.
5
4.
0
4
.
5
4.
5
5
.
0
9
5
.
4
8
5
.
5
5.
45
5.
5
5.
8
Store this qu
ery table in the memory of S7
-CP
U
31
5-2DP. In real-t
ime control p
r
ocess,
the pro
g
ram
sea
r
che
s
this query ta
ble
dire
ctly and g
e
ts the
cont
rol value
∆
u
ij
according to t
h
e
value of fuzzf
ied error a
n
d
erro
r rate, th
en
multiply it by the prop
ortional fa
cto
r
K
u
,this result
can b
e
used to control the controlled pl
a
n
t as output value.
3.2. The Desi
gn of Adju
stable Fac
t
or
The p
r
op
orti
onal fa
ctor o
n
-line
self-ad
j
ustment m
e
thod
wa
s em
ployed in thi
s
fuzzy
c
ontroller
.
As c
o
nventional c
o
ntr
o
l, fuz
zy c
ontro
l is
s
t
ill has
c
ontradic
t
ion bet
w
e
en its s
t
atic
and
dynamic
cha
r
acte
ri
stics. So, if we adjust t
he thre
e para
m
eters simultane
ou
sly, the cont
rol
algorith
m
will
be too
com
p
lex. From
controlle
r’
s st
ructure
we
ca
n find that th
e cau
s
ality o
f
adju
s
ting
K
u
should
be
clea
rer and
we sti
ll can
re
ach o
u
r p
u
rp
ose of
adju
s
ting
K
e
and
K
c
finally.
In orde
r to g
e
t the best
contro
l p
e
rfo
r
mance, we
chose setting
K
e
and
K
c
off-line while s
e
tting
K
u
on-line [12].
The pri
n
ci
ple
block dia
g
ra
m has
sho
w
n
in Figure 2.
Figure 2. Block di
agram of
se
lf-adj
ustin
g
fuzzy controller
Figure 3. Erro
r cha
ngin
g
cu
rve
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TELKOM
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Re
sea
r
ch on
a Kind of PLC Base
d Fu
zzy-PID C
ontroller with Adj
u
stabl
e Facto
r
(Wei Xie)
301
From te
sts
we g
o
t erro
r’s chan
ging t
r
end
and t
h
e
n
drew
out its chan
ging
curve a
s
s
h
ow
n
in
F
i
gu
r
e
3
.
In point a, e(t)
﹥﹤
0, larger
and de/dt
0,
in orde
r to get rid of error ra
pidly we
need
st
ron
g
e
r
co
nt
rol ef
f
e
ct
,
so
we ma
ke
K
u
larger.
In point b,
e(t
)
is ne
arly re
achi
ng
stead
y value an
d
de/dt
﹤
0, to a
v
oid e(t) da
shing ove
r
the set value
and lea
d
to new fluctu
ation
,
we hope
K
u
can be
small
e
r.
In point
c
,
e(t)
﹤﹤
0
and
de/dt
0,
f
o
r
a
c
cele
rati
ng
the
co
nve
r
gen
ce
spe
e
d
of e,
we
need
K
u
u
larger.
In point
d, e
(
t)
﹤﹥
0
,
de/
dt
0,
the co
ntrol effect shoul
d be wea
k
e
r
to
void
big
ove
r
turnin
g,
so
K
u
sho
u
ld
be small
e
r.
Similarly, we
can
analy
s
e
K
u
values
i
n
other point
s. In
this way, we can
get a
gro
up o
f
fuzzy rule
s a
bout
K
u
’s val
ues,
acco
rdin
g to the
s
e
rul
e
s, a
que
ry t
able a
bout
K
u
’
s value ca
n
be
built. The gen
eral form of t
hese rule
s is:
if
E
=
A
i
and
EC
=
B
i
, then
K
u
’
=
C
i
(i=
0
, 1, 2,
....
..n)
Whe
r
e
K
u
’
sh
ould satisfy the equ
ation:
K
u
’
=
K
u
’
K
uo
. In this equat
ion
K
uo
mean
s setting
off-line and
K
u
’
means
sea
r
chin
g in fuzzy value table.
We o
b
tained
K
u
’
s query t
able in the
same way as
we got the fu
zzy
controller’s que
ry
table, both ge
nerate
d
off-lin
e in MATLAB as sh
own in Table 3.
Table 3. The
Query Ta
ble
of Ku’
Error
e
Error
Ra
te
ec
-
6
-
5
-
4
-
3
-
2
-
1
0123
4
5
6
-
6
6
5
.
6
5
5
4
4
4322
2
2
1
-
5
5
.
6
5
.
5
5
4
4
3
3222
1
.
5
1
1
-
4
5 5
4 4 3
3
2
.
5
2
2
1
.5
1
1
1
-
3
5 5
4 4 3
2
.5
2
1
.5
1
1
1
2
2
-
2
5
5
4 3
.
5
3
2
.5
2
1
.5
1
1
1
.
5 2
3
-
1
4
4
3
.
5
3
2
.
5
2
2
1
1
1
.
5
2
2
3
0
4 3
3
2
.
5
2
1 1
1
2
2
.
5
3
3 4
1
3
2 2
2 1
1
2
2
2
.
5
2
3
.
5
4
4
2
3 2
1
.
5
1
1
1
.5
2
2
.5
3
3
.5
4
5 5
3
2 2
1
1 1
1
.5
2
2
.5
3
4
4
5
5
4
1 1
1
1
.
5
2
2
2
.
5
3
3
4
4
5 5
5 1
1
1
.
5
2
2
2
3
3
4
4
5
5
.
5
5
.
6
6
1
2 2
2 2
3
4
4
4
5
5
5
.
6
6
4. Results a
nd Analy
ses of the Expe
r
i
ment
Becau
s
e te
m
peratu
r
e i
s
in
fluenced by
outsid
e
environment, the i
n
itial tempera
t
ures
of
different tim
e
s a
r
e diffe
rent. In ord
e
r
to in
cre
a
se experi
m
en
t’s co
mpa
r
a
b
ility, we made
temperature
cha
nge
d in a
sam
e
range
in ever
y exp
e
r
iment. Th
e sampling tim
e
of temperature
controller is t=2s.
The a
n
aly
s
e
s
of these
cont
ro
lle
rs a
r
e a
s
fo
llowin
g
:
(a) PID
co
ntrolle
r. T
he t
e
m
p
er
ature
variation ra
n
ge
i
s
1
4
℃
－
18
℃
, dashe
d
l
i
n
e
rep
r
ese
n
t
s
set
val
u
e
.
T
h
ro
ug
h
Fi
gu
re
4
we ca
n
see t
h
e rise-tim
e
is
t
γ
=18
0
s
and
the ove
r
sho
o
t is
σ
%=15
.2
5%, sta
b
l
e
ra
ng
e
is
bet
wee
n
±0
.45.
(
b
)
F
u
z
z
y
co
n
t
ro
ller
.
T
h
e
temper
a
t
ur
e var
i
a
t
ion ra
ng
e is
1
8
.
1
℃
－
22
.1
℃
.Th
r
o
ugh
Fi
gu
re 5
we
ca
n see
t
he ri
se
-t
i
m
e
is
t
γ
=12
6
s
an
d th
e
overshoot i
s
σ
%=10%
, stable ra
nge
i
s
b
e
twe
en
±0.1
6. Th
is
syste
m
is
sta
b
le a
nd t
h
e
ri
se-ti
m
e is
shorte
r.
(c)
F
u
zzy PID
co
nt
rolle
r.
The tem
p
e
r
at
ure
v
a
riatio
n ra
n
ge is
2
2
℃
－
26
℃
.T
hro
u
g
h
Fi
gu
re
6
we
c
an
k
n
ow
th
is
s
y
s
t
em
is
stab
le
a
n
d
t
h
e
rise
-ti
m
e
is
sh
ort t
o
o,
wh
at’s
m
o
re, it
h
a
s
a
b
e
t
ter
steady
pre
c
i
s
ion
t
h
an
co
nve
n
tio
nal fu
zzy co
nt
roll
e
r
. Th
e
ri
seti
m
e
is
t
γ
= 106
s
a
n
d t
h
e
ov
ersho
o
t is
σ
%=8.6%, stable ra
nge
is
betw
e
e
n
±0
.0
6.
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ISSN: 23
02-4
046
TELKOM
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KA
Vol. 14, No. 2, May 2015 : 298 – 303
302
Figure 4. Te
mperature
re
spo
n
s
e
cu
rv
e
of
P
I
D
controller in 4
Ԩ
Figure 5. Te
mperature
re
spo
n
s
e
cu
rv
e
of
f
u
zzy
controller in 4
Ԩ
Figure 6. Te
mperature
re
spo
n
se cu
rv
e
of fuzzy
-PID
contr
o
lle
r in 4
Ԩ
5. Conclusio
n
In
this pag
e, we
to
ok
AE2
000A cent
ral contro
l
syste
m
’s b
o
ile
r te
mperature
a
s
co
ntrolle
d
plant, gave t
he an
alysi
s
and
comp
ari
s
on
on
cont
rol re
sults ba
sed
on PID
controlle
r, fuzzy
controlle
r an
d fuzzy PID controlle
r. We found that
fuzzy
PID co
ntrolle
r
with adju
s
table
fa
ctors
has
obviou
s
advantag
es over the
other t
w
o.
I
t
has
a bet
ter dynami
c
-static
re
spo
n
se
cha
r
a
c
teri
stic and stro
nge
r robu
stne
ss,
so it can g
e
t rid of system’
s
re
sidu
al error. We
can g
e
t
the co
ncl
u
si
o
n
that fuzzy PID co
ntrol i
s
an effect
ive method
in de
aling with
time-varyin
g
pro
c
e
ss
control proble
m
s with la
rge
time-delay.
Ackn
o
w
l
e
dg
ements
The resea
r
ch wo
rk was
sup
porte
d by
gene
ral
pro
g
ram
of scie
nce
and
technolo
g
y
developm
ent proje
c
t of Beijing Muni
cipal
E
ducation Commissio
n under g
r
ant K
M
2015
108
58
004
and key prog
ram of Beijing Polytechni
c u
nder g
r
a
n
t YZKB20140
08.
Referen
ces
[1]
F
G
Hinske
y
.
P
r
ocess Co
ntrol
S
y
stems- Ap
plicat
i
on, Des
i
gn, and T
unin
g
. Xia
o
De
yu
n
,
Lv Boming.
Beiji
ng: T
s
ingh
ua Un
iversit
y
P
r
ess.
[2]
Bolat ED, Erk
an K, Postalcioglu S.
Experi
m
e
n
tal Aut
o
tu
nin
g
PID Cont
rol of T
e
mper
ature Usi
n
g
Microcontr
o
ll
er
.
T
he Internatio
nal C
onfere
n
ce
EUROCON. 2005; 1: 26
6-26
9.
[3]
He SZ, Xu FL,
T
an S.
A new
ada
ptive S
m
ith pre
d
ictor c
o
n
t
roller
. 1992 IEEE Region
10 Internationa
l
Confer
ence. 1
992; 2: 10
38-1
042.
[4]
Chia-F
e
ng J
u
ang, Ju
ng-S
h
i
ng C
hen, H
ao-Ju
ng H
u
a
ng.
T
e
mper
ature co
ntrol
by har
dw
ar
e
imple
m
ente
d
recurre
nt fu
zz
y
controll
er.
Proceedings 2004 IEEE Inter
national Confer
enc
e. 2004; 2:
795-
799.
[5]
Ingram JE, Ho
del AS, Kirkic
i
H.
Robust te
mperatur
e contr
o
l in th
e
meas
ure
m
e
n
t of hig
h
temper
atur
e
vapor pr
essure
s
. IECON 97, 23rd Internati
o
n
a
l
Co
nferenc
e. 199
7; 1: 149-1
54.
[6]
Khal
id M, Om
atu S. A
ne
ura
l
n
e
t
w
ork
c
ontr
o
ller
for
a tem
peratur
e c
ontr
o
l s
y
stem.
Co
ntrol Syste
m
s
Maga
z
i
ne
,
IEEE
. 1992; 12(
3): 58-64.
[7]
Han-
Xi
on
g
Li,
Gatlan
d H
B
. Co
nventi
o
n
a
l
fuzz
y
co
ntrol
an
d its
en
ha
nceme
n
t S
y
stems, Man
an
d
C
y
ber
netics, Part B.
IEEE Transactions.
199
6; 26(5): 79
1-7
97.
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Re
sea
r
ch on
a Kind of PLC Base
d Fu
zzy-PID C
ontroller with Adj
u
stabl
e Facto
r
(Wei Xie)
303
[8]
Chu Ji
ng etc. Princip
l
e a
nd Ap
plicati
on of F
u
z
z
y
Co
ntrol. Bei
j
i
ng: Ch
ina Mac
h
in
e Press. 19
99.
[9]
Lia
o
Cha
ngc
hu
. Applicati
on T
e
chn
o
lo
g
y
of S7-30
0
/40
0
PLC.
Beiji
ng: Chi
na
Machi
ne Press
.
2005.
[10]
Kun Zh
e. Win
CC-E
x
pl
ai
nin
g
t
he Pr
ofou
nd
of SIMENS Wi
nCC
in
Si
mp
le
La
ngu
ag
e. Be
ijin
g: Be
iji
n
g
Univers
i
t
y
of Aeron
autics a
n
d
Astronautics P
r
ess. 2004.
[11]
Z
hang Guo
lia
n
g
, Z
eng Jing, Ke Xiz
h
e
ng. F
u
zz
y
C
ontrol a
nd Its Applicati
on
in MAT
L
AB. Xi’a
n: Xi'
a
n
Jiaoto
ng U
n
ive
r
sit
y
Press. 20
02.
[12]
Sun Ze
ngq
i, et
al. T
heor
y a
n
d
T
e
chniq
ue
o
f
Inte
llig
ent C
o
ntrol. Bei
jin
g:
T
s
inghua Univ
ersit
y
Press.
Guang
xi: Guan
gxi Univ
ersit
y
Press. 2004.
Evaluation Warning : The document was created with Spire.PDF for Python.