TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol. 14, No. 3, June 20
15, pp. 470 ~ 4
8
0
DOI: 10.115
9
1
/telkomni
ka.
v
14i3.795
2
470
Re
cei
v
ed
No
vem
ber 2, 20
14; Re
vised Janua
ry 2
6
20
15; Accepted
February 15,
2015
Artificial Optimal Fuzzy Control Strategy for Elevato
r
Drive System b
y
Using Permanent Magnet
Synchronous Motor
S
a
lam Wale
y
1
,
Ch
e
n
gxio
ng M
a
o*
2
State Ke
y
L
a
b
o
rator
y
of Adva
nced El
ectrom
agn
et
ic Eng
i
ne
erin
g and T
e
ch
nol
og
y, W
uha
n
,
C
h
i
n
a
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: salam_
w
a
l
e
y73@
ya
ho
o.co
m
1
,
cxmao
@h
u
s
t.ed
u
.
c
n
2
A
b
st
r
a
ct
T
here ar
e
ma
ny pow
er
elec
tronic co
nverte
rs and
mo
tor
drives c
onn
ect
ed tog
e
ther t
o
form th
e
electric
al syste
m
of an el
evat
or. In this pape
r, w
e
have presented a
mo
de
ling to
ol
that h
a
s the adva
n
ta
ges
of utili
z
i
n
g
c
a
p
abil
i
ties
of the
PMSM software in
deta
ile
d
simulati
ons of
converters,
motor drives, a
n
d
electric
mac
h
in
es. In additi
on,
equiv
a
le
nt ele
c
trical mo
d
e
ls
of Elevator driv
e system. This
paper a
l
so g
i
v
e
s
a bri
e
f id
ea
of
PMSM vali
dity
as an
el
evator
simula
ti
on t
ool.
PMSM driv
e s
ystem
is d
e
scri
bed
an
d a
n
a
l
y
z
ed
due
to its
i
m
p
o
r
tance
in
many
ap
plic
atio
ns e
s
peci
a
lly
in
el
e
v
ator a
ppl
icati
o
ns. Ap
p
licati
o
n
s
are
cons
id
ere
d
due to their
high efficiency,
low inertia and
high tor
que to v
o
lum
e
r
a
tio. A
closed loop c
ont
rol system
with a
PI control, F
u
zz
y
,
PSO i
n
t
he sp
ee
d l
o
o
p
w
i
th curr
ent
control
l
ers. T
h
e si
mu
latio
n
ci
rcuits for PMS
M
,
inverter, s
pee
d
an
d curr
ent c
ontrol
l
ers
incl
u
de
all
re
a
listic
co
mp
one
nts
of the
drive
sy
stem. Si
mulati
on
results for SP
WM control sc
hem
e
s
a
ssoci
a
t
ed w
i
th curren
t
controll
ers ar
e giv
en for tw
o spee
ds, on
e b
e
low
rated a
nd a
not
her ab
ove rat
e
d spee
d. In thi
s
paper
an
d it has be
en s
h
o
w
n that t
he mo
del is s
u
itab
le
for
transie
nt as w
e
ll
as ste
ady
state
con
d
iti
o
n
.
T
hese r
e
sult
s als
o
co
nfir
med th
at the
tra
n
sie
n
t torq
ue
and
current nev
er e
xceed the
max
i
mu
m p
e
r
m
issi
b
l
e val
ue.
Ke
y
w
or
ds
:
e
l
e
v
a
t
o
r
d
r
i
v
e
s
y
s
te
m
,
pe
rm
an
en
t m
a
gn
e
t
s
y
n
c
h
r
o
no
u
s
m
o
tor
s
(
P
M
S
M
)
,
PI
c
o
n
t
r
o
l
l
e
r
, fuzzy
l
o
g
i
c
(
F
L
)
,
partic
l
e sw
ar
m o
p
ti
mi
z
a
t
i
on (P
SO
)
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. Introduction
The elevato
r
s are be
com
e
more im
po
rtant
with Increa
se the b
u
ilding
s
and t
o
we
ring
sky
scrap
e
rs, it
mean
s of
trans
port
bet
ween l
e
ss th
e
uppe
r flo
o
rs
and de
cks,
t
he
el
evators for
spe
c
ial the m
a
in mean
s of
transp
o
rt, si
mplified
co
ntrol and elevat
ors to de
al efficiently dem
and
for trans
p
ort is
complex [1]
.
The PMSM, it has
s
i
gnifi
cant a
d
vanta
ges, attractin
g
the inte
re
st of
resea
r
chers a
nd indu
stry fo
r us
e in ma
ny appli
c
ation
s
, that us
e
s
p
e
rmanent m
agn
ets to produ
ce
the air gap
magneti
c
field rathe
r
than
using
ele
c
tromagn
ets [2]. The PMSM with high le
vel
energy pe
rm
anent ma
gne
t material
s p
a
rticul
arly
p
r
o
v
ide fast dyn
a
mics, efficie
n
t operation
and
good
compati
b
ility with the
appli
c
ation
s
but only if
th
e
y
are
controll
ed p
r
op
erly.
The
cont
rolle
r i
s
usin
g in o
r
d
e
r to ove
r
co
me the no
n
linearity
p
r
ob
lem of PMSM and al
so t
o
achieve fa
ster
respon
se
[3]. Many ind
u
strial appli
c
atio
n
s
requi
re
n
e
w cont
rol te
chn
i
que
s, the te
chniqu
es
used
,
applie
d in
all
re
gulation
lo
ops, S
peed
regulat
io
n of
perm
ane
nt m
agnet
syn
c
hronou
s [4]. T
h
e
developm
ent of powe
r
ele
c
tronics an
d el
ectri
c
it
y Tech
nology, PMSM for extensi
v
e applicatio
ns
in many con
t
rol Systems.
And PMSM, whic
h are
commonly u
s
ed for Syste
m
s and
cont
rol
device
s
min
u
te own
s
se
veral Advant
age
s over
ot
her ma
chi
n
e
s
Milan. Advantage
s PM
SM
inclu
de la
rge
torque
co
efficient, and hi
gh
efficien
cy, high en
ergy d
e
n
sity, and a t
o
rqu
e
multipli
er
is sm
all, Low-inertia, lo
w n
o
ise, an
d hig
h
perfo
rma
n
ce in a w
i
de var
i
ety [5]. A w
a
y c
ontrol (
P
I)
in
addition to th
e co
ntrolle
r’
s integral
rel
a
tive
formulate
d
and im
ple
m
ented, u
s
in
g sp
eed
co
ntrol
magnet
syn
c
hron
ou
s mot
o
r d
r
ive
syste
m
and
a
p
e
rmanent pilot pha
se. While
the
ne
w stra
tegy
prom
otes tra
d
itional PI co
ntrol pe
rform
ance to
a large extent, an
d prove
s
to
be a mod
e
l-f
r
ee
approa
ch
co
mpletely, it also
kee
p
s the
stru
cture an
d feature
s
of
a simpl
e
PI control [6]. The
usin
g co
nsol
e mode in
st
ead of Fu
zzy-PI contro
l i
s
to improve
the perform
ance of engi
nes
PMSM. To control the
sp
eed of PMS
M
motor u
s
in
g fuzzy logic
(FL
)
app
ro
ach lead
s to a
spe
e
d
control to im
prove the dy
namic
behavi
o
r of t
he mot
o
r drive
syst
em and imm
une di
so
rde
r
s to
download
an
d pa
ramete
r variation
s
[
7
]. In the
el
evator d
r
ive
system
s,
an
d gain
s
from
the
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Artific
i
al Optimal Fuz
z
y
Control Strategy for El
evato
r
Dri
v
e System
by Using
…
(Salam
Waley)
471
traditional
ca
n not usuall
y
be set in prop
ortio
n
-int
egral
(PI) co
ntrolle
r spe
e
d large e
nou
gh
becau
se of m
e
ch
ani
cal
re
sonan
ce. A
s
a
re
sult,
Perfo
r
mance d
egra
dation a
nd
sp
eed
cont
rol. I
n
our wo
rk
d
e
scrib
ed
i
n
thi
s
pape
r,
ha
s b
een ado
pted and
fu
zzy
log
i
c controlle
r
(FLC)
fo
r
u
s
e
in
Elevator driv
e system
s in
orde
r
to improve the perfo
rman
ce of th
e spe
ed control. The pro
p
o
s
ed
FLC h
a
s
bee
n com
p
a
r
ed
with traditio
n
a
l PI control
with re
sp
ect t
o
the spee
d
of re
spon
se
and
dynamic loa
d
Torque. Sim
u
lation a
nd
e
x
perime
n
ta
l result
s h
a
ve p
r
oved th
at FL
C
wa
s p
r
op
o
s
ed
is supe
rio
r
to
the traditio
n
a
l PI. This F
L
C
can
be a
good
sol
u
tio
n
for the
hig
h
-pe
r
forman
ce
engin
e
lifts Systems [8, 9]. A modern approa
ch to
control the speed of PMSM using p
a
rti
c
le
swarm
optimi
z
ation
(PSO) is to imp
r
ov
e the al
g
o
rith
m paramete
r
s ob
se
rver P
I. Simulate the
system u
nde
r different ope
rating yea
r
Condition
s a
r
e
prep
ared an
d
the experim
e
n
tal setup.
Use
PSO algo
rith
m and
optim
ization
ma
ke
s a
po
werful
engin
e
, with
faster re
sp
o
n
se
and
hig
her
resolution dy
namic a
nd se
nsitive to load
variation [10, 11].
2. Model for a PMSM Dri
v
e
The co
mplete
nonline
a
r mo
del of a PMSM without da
mper
windi
ng
s is a
s
follows:
)
+
i
(L
+
i
pL
+
Ri
=
v
af
d
d
s
q
q
q
q
(1)
i
L
-
p
+
Ri
=
v
q
q
s
d
d
d
(2)
v
d
and v
q
are
the d,q axi
s
v
o
ltage
s, id a
n
d
iq a
r
e th
e d
,
q axis
stater
curre
n
ts, L
d
and L
q
are
the d,q axis
ind
u
cta
n
ce, R
an
d
s
are the
sta
t
er re
si
stan
ce and i
n
vert
er fre
que
nc
y
r
e
spec
tively.
af
is the flux linkage du
e to the
rotor m
agn
ets linki
ng the
stator.
Figure 1. Block di
agram of
a PMSM
Figur
e 2. Block di
agram of
a PMSM Dri
v
e
The elec
tric
torque:
)/2
i
)i
L
-
(L
i
3P(
T
q
d
q
d
q
af
e
(3)
The moto
r dynamics:
r
r
Jp
B
T
-
T
L
e
(4)
P is the
num
ber
of pole
p
a
irs,
TL i
s
th
e load
torq
ue
, B is the
da
mping
co
efficient,
r
is
the roto
r sp
e
ed and
J the
moment of in
ertia. The in
v
e
rter frequ
en
cy is related t
o
the roto
r sp
eed
as
follows
:
r
s
p
(5)
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 14, No. 3, June 20
15 : 470 – 48
0
472
The ma
chine
model is n
onl
inear a
s
it co
ntai
ns p
r
od
uct terms su
ch
as speed
with id and
iq. Note that
r
, i
d
and i
q
are
state varia
b
l
e
s. Durin
g
ve
ctor
cont
rol, i
d
is normally
forced to be
zer
o
.
q
t
q
e
i
K
/2
i
3P
T
af
(6)
3. Speed Control of PMSM Motor
The PMSM is using
control
to suppress
harm
oni
c noi
se to a level. then, noise to a level
belo
w
and vibration tra
n
sl
ates into a more
comfortable ride for passeng
ers.
IGBT SPWM
inverters ma
ke
th
e ride
smoothe
r with
preci
s
el
y
ad
justing
spee
d
co
ntrol
with
freq
uen
cy a
nd
voltage re
gul
ation. It has t
he late
st low-noise po
we
r
units to m
a
ke
the ride
even
quieter.Eleva
tor
has dire
cted high-sp
eed u
s
ed (15
00 rp
m)
PMSM.
Ener
gy
reform
in the elevato
r
gea
re
d for
small
rise b
e
cau
s
e
travel extrem
ely small and
fast.
Figure 3. Block
Diag
ram o
f
Speed Cont
rol of PMSM
3.1. PI Contr
o
ller Modeling
In the PI spe
ed controll
er
the motor
sp
eed i
s
comp
ared
with the
referen
c
e
sp
eed a
n
d
the spe
ed error is the nth
sampling inte
rval as:
[n]
-
[n]
*
[n]
r
r
e
(7)
The
o
u
tput
of
the spe
ed controlle
r gives
th
e
refe
re
n
c
e to
rqu
e
. Hence the
out
put of the
spe
ed control
l
er at the nth sampli
ng inte
rval is:
[n]
Ki
1])
-
[n
-
[n]
Kp(
1]
-
T[n
T[n]
e
e
e
(8)
For con
s
tant air gap flux o
peratio
n refe
r
ence qua
drat
ure axis
cu
rre
nt is given as:
T[n]/Kt
*
i
q
(9)
The limite
r
i
s
u
s
ed
to li
mit the maxi
mum value
of output of
spe
ed
cont
ro
ller. The
maximum mo
tor rated
cu
rrent and devi
c
e curr
e
n
t of the co
nverte
r dictate the li
mit.
Whe
r
e,
ω
e[n] is spe
e
d
error at nth
instant,
ω
r*[n]
is the referen
c
e spee
d at nth instant;
ω
r[n] is the
actual m
o
tor spee
d at nth instant,
ω
e
[
n-1] is the
speed e
r
ror at
(n-1
)th
instant;
T[n] is th
e
referen
c
e
torq
ue at
nth i
n
stant, T[n-1] i
s
the
referen
c
e to
rq
ue
at (n
-1
)th
instant;
Kp is pro
porti
onal gai
n of the sp
eed
con
t
roller;
Ki is integral
gain of the sp
eed controll
er is referen
c
e
quad
ratu
re a
x
is curre
n
t;
Kt is torque consta
nt
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Artific
i
al Optimal Fuz
z
y
Control Strategy for El
evato
r
Dri
v
e System
by Using
…
(Salam
Waley)
473
3.2. Fuzzy
Logic Con
t
rol
l
er
The Ba
sic configuration
of a Fu
zzy
L
ogic
Co
ntroll
er (FL
C
)
co
n
s
ist
s
of the f
o
llowin
g
comp
one
nts:
1) Fuz
z
i
fic
a
tion Interfac
e
2
)
Kn
ow
le
dg
e
Bas
e
(KB)
3) De
ci
sion M
a
kin
g
Logi
c
4) Defuzz
ific
ation Interfac
e
A fuzzy controller i
s
a
spe
c
ial fu
zzy
system
that can
be u
s
ed a
s
a
controlle
r co
mpone
nt
in a closed-l
o
op system. T
he integr
ation
of a fuzzy system into a
cl
ose
d
loop is
sho
w
n. Spe
c
i
a
l
empha
si
s is
put onto the
transfe
r be
haviour of
fu
zzy controll
e
r
s, whi
c
h i
s
analyzed usi
n
g
different co
nfiguratio
ns
of st
andard m
e
m
bership
fun
c
tions. A
n
exa
m
ple fo
r the
desi
gn
of a fu
zzy
controlle
r fo
r a l
oadin
g
crane
i
s
give
n. Finally, th
e mo
dule
se
ries i
s
clo
s
e
d
by
a g
ene
ral
discu
ssi
on
ab
out the
contri
bution
of fu
zzy cont
rol.
F
o
r
a PM m
o
tor d
r
ive
system
with a full
spee
d
rang
e, the system will
consi
s
t of a
motor, an
in
verter, controller
(consta
nt torque
an
d flux
wea
k
e
n
ing o
peratio
n, gen
eration of
referen
c
e
cu
rre
nts and PI co
ntrolle
r).
3.3. Particle S
w
arm Opti
miz
a
tion
It is a techniq
ue use
d
to explore the
sea
r
ch spa
c
e of a given proble
m
to find the
setting
s
or pa
ramete
rs req
u
ired to optimize a p
a
r
ticula
r
obj
ect
i
ve. PSO has two ma
in conc
epts
: the firs
t
is thro
ugh th
e observatio
n
of human d
e
ci
sion m
a
ki
ng, it was p
r
opo
sed that
human
s u
s
e
both
their o
w
n be
st experien
c
e
and othe
rs’ b
e
st expe
rien
ce to form a b
a
si
s of maki
n
g
a de
cisi
on, to
develop the concepts of in
dividual lea
r
n
i
ng and cult
ural transmissi
on. The se
co
nd is to prop
ose
a simpl
e
the
o
ry to explai
n gro
up b
e
h
a
vior in n
a
ture, and to m
o
dulari
z
e th
e theory to
cre
a
te
sy
st
em
s t
o
si
mulat
e
t
h
ing
s
.
The bigge
st
cha
r
a
c
t
e
ri
st
ic of
P
S
O
is
in it
s simpl
e
st
ru
ct
ur
e,
f
a
s
t
conve
r
ge
nce, and its abilit
y to prevent falling into
a l
o
cal o
p
timum
solution. At the sa
me time,
PSO is a ran
dom algo
rith
m with a parallel stru
ctu
r
e
.
Firstly, a uniform di
strib
u
tion is u
s
ed
to
rand
omly cre
a
te a particl
e swarm.
Ea
c
h
p
a
r
t
ic
le
r
e
pr
es
en
ts
a
fe
a
s
ib
le
s
o
lutio
n
to the problem, the part
icle swarm
refers to
the individ
ual
’s b
e
st
expe
rience, an
d th
e g
r
oup’
s
be
st expe
rien
ce
, and l
ogi
call
y cho
o
ses th
e
method it will
move it
self.
Afte
r continu
ous iteration
s
, the p
a
rtic
l
e
swarm
will
g
r
avitate towa
rds
the optimum
solutio
n
.
3.4. Ele
v
ator State Sampl
e
s
In Figure 4
we find this sta
t
es:
a) [1-2,2-3,3-2,2-1]; b)
[1-2,2-3,3-1]; c
)
[1-3
,3-1]; d) [1-3,3-2,2-1]
1=1
st
floor, 2=
2
nd
floor, 3=3
rd
floor, 1-2=1
st
floor to 2
nd
floor, 2-3=2
nd
floor to 3
rd
floor,
2-
1=2
nd
floor to1
st
floor, 3-2= 3
rd
floor to 2
nd
floor, 1-3=
1
st
floor to 3
rd
floor,3-1=
3
rd
floor to 1
st
floor ,We
can
write anoth
e
r
sam
p
le[1-2-1]that is mea
n
: 1-2=1
st
floor to 2
nd
floor and 2-
1=2
nd
floor to1
st
floor
Figure 4. Elevator state sa
mples a
)
[1-2,
2
-3
,3-2,2-1], b) [1-2,2-3
,3-1], c
)
[1-3,3-1],
d) [1-3,3-2,2-1]
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0
474
3.5. Ele
v
ator Contr
o
l Sy
s
t
em Applica
t
ion Flo
w
Ch
art
The
Steps
as follows
(
f
low
c
h
ar
t a
s
F
i
gu
re
5
)
:
Step 1: Creat
e the para
m
e
t
ers of the ele
v
ator.
Step 2: Acco
rding to the ca
ll signal
a
nd the cu
rrent sit
uation Elevator.
Step 3: by Reload
squ
a
d
r
on by usi
ng
update va
lu
e-weig
hted ine
r
tial, with the spe
ed of
conve
r
ge
nce.
Step 4: use t
he a
c
ceptan
ce crite
r
ia, to
deci
de wheth
e
r to a
c
cept t
hese ne
w pa
rticles
or
not, and to incre
a
se the di
versity of the particl
es, with
avoid trappin
g
in local O
p
timization.
Step 5: The end of the iterati
on, then the global sea
r
ch the optima
l
solution. If not, step
loop (3
).
Step 6: by using Loo
p to step (2
) until the
end time si
mulation,then
output the re
sult.
Figure 5. Elevator cont
rol
sy
stem a
pplication flow cha
r
t
4. Simulation Resul
t
s
By using Si
mulation mo
del PMSM&Simulation
of
Elevator Drive
system by
using
PMSM:
4.1. Simulation Model Pe
rmanent M
a
gnet Sy
nchronous Mo
tor
(PMSM)
Model of the
system in
Fig
u
re 6 i
s
verifi
ed thro
ugh th
e com
pute
r
simulation
s u
s
i
ng the
softwa
r
e
pa
ckag
e MATLA
B
/Simulink. It sum
m
ari
z
e
s
the pe
rform
ance of the
elevator’
s
el
e
c
tric
drive, both in comp
uter
simulatio
n
an
d exper
ime
n
tal implement
ation. The analyze
d
elevator
con
s
id
ers el
e
c
tri
c
al d
r
ive
(PMS
M Drive
System). El
evator m
o
tor is th
ree
-
ph
a
s
e
pmsm
with.
Drive
conve
r
ter is
cu
rrent
regul
ated S
P
WM vo
ltag
e so
urce inv
e
rter
(CRSP
W
M VSI) di
rect
curre
n
t po
wer sup
p
ly. The
positio
ning
sy
stem an
d po
sition co
ntrolle
r of Elevator
are u
s
e
d
for t
he
task to
provid
e po
sition referen
c
e t
r
a
cki
ng an
d ze
ro
error in
stea
d
y
state. Con
s
tant load i
s
u
s
ual
for one
elev
ator ri
de. Th
us, a po
sitio
n
co
ntrolle
r
with propo
rtional an
d inte
gral a
c
tion
(PI) is
use
d
, Block
diagram of P
I
Controll
er i
s
sho
w
n in
Figure 7. Parameters of real elevato
r
with
pmsm d
r
ive were incl
ude
d in the mode
l of elevator.
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TELKOM
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ISSN:
2302-4
046
Artific
i
al Optimal Fuz
z
y
Control Strategy for El
evato
r
Dri
v
e System
by Using
…
(Salam
Waley)
475
Simulation of
the entire
sy
stem with
de
sign
ed
contro
ller is m
a
de i
n
the Matlab/
S
imulink
and given re
sults
sho
w
that design
co
ntrolle
r
meet
s the req
u
ire
m
ents compl
e
tely smooth
and
pre
c
ise po
sition and
spe
e
d
.Input param
eters
fo
r the dynamic of th
e elevator a
r
e
:
a)
Final po
sition
of car: 9 m,
b)
Nomin
a
l sp
ee
d of car: 1.5
m/s,
c)
Elevator ca
pa
city: 1050 kg,
d)
Weig
ht of the car: 450
kg.
To verify the
feasibility of
control, PMSM dr
ive
simul
a
tion model
with control i
s
created
and
studie
d
usin
g MATLA
B
. Simulation param
ete
r
s: stator re
sista
n
ce Rs
=
0.0
1
Ω
, indu
ctan
ce
Ld = L
q
= 0.
0183
5H, flux
Ψ
= 0.4 V.s,
pair of pol
es
p = 3,
ine
r
tia
J = 0.02
9kg.m2. Simulation
con
d
ition
s
: re
feren
c
e spee
d n = 1500 ra
d / s, start with TL = 5N.m. Simulation re
sults a
r
e sho
w
n
in Figure 8-1
0
spe
ed is
sh
own in Fi
gure
8, Torque
i
s
sho
w
n in Fi
g
u
re 9, an
d cu
rre
nt is sh
own in
Figure 1
0
. It is o
b
viou
s tha
t
corre
c
t resp
onses of
spe
ed, current, a
nd torque
in
control
syste
m
.
Usi
ng PI co
n
t
rol and
Fu
zzy control
ha
s a good
appli
c
ation i
n
PMSM drive. At the sam
e
tim
e
,
with sp
eed
have faste
r
respon
se.
Ri
pple of to
rq
ue is obvio
u
s
ly red
u
ced.
So the system
perfo
rman
ce i
s
improved.
Figure 6. Model of the system
Figure 7. PI
Controller
Figure 8. Speed Re
sp
on
se
, n=150
0ra
d
/se
c
Fi
gure 9. Torque Respon
se, n=15
00rad
/sec
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Vol. 14, No. 3, June 20
15 : 470 – 48
0
476
Figure 10. Cu
rre
nt Re
spo
n
s
e, n=150
0ra
d
/se
c
&TL
=
5
N
4.2. Simulation of Elev
at
or Driv
e S
y
s
t
em b
y
usin
g PMSM
(a) Spe
e
d
(b) T
o
rq
ue
(c
)
Figure 11. Speed, Torque
& Cu
rrent Re
spo
n
se, n = (0 ,1500)ra
d / s &TL = 10
N.
m
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Artific
i
al Optimal Fuz
z
y
Control Strategy for El
evato
r
Dri
v
e System
by Using
…
(Salam
Waley)
477
(a) Spe
e
d
(b) T
o
rq
ue
(c) Pha
s
e cu
rrent
Figure 12. Speed, Torque
&
Current Re
spo
n
se, n=(0,1500 ,0)ra
d
/s &TL = 10N.
m
(a) Spe
e
d
(b) T
o
rq
ue
(c) Pha
s
e cu
rrent
Figure 13. Speed, Torque&
Current Respon
se,
n = (0,1500,0,1
5
0
0
,0,-150
0,0,-1500,0
)
rad / s
&TL = 10
N.m
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TELKOM
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Vol. 14, No. 3, June 20
15 : 470 – 48
0
478
(a) Spe
e
d
(b) T
o
rq
ue
(c) Pha
s
e cu
rrent
Figure 14. Speed, Torque&
Curre
n
t Re
sp
onse, n = (0,
1500,0,1
500,
0,-150
0,0,-1
5
00,0)rad /s &
TL = (1
0,5,10
,5)N.m, a Torque is vari
abl
e
(a) Spe
e
d
(b) T
o
rq
ue
(c) Pha
s
e cu
rrent
Figure 15. Speed, Torque
& Current Re
spo
n
se,
n = (0,1500,0,1
5
0
0
,0,-150
0,0,-1500,0
)
rad / s
&TL = 0N.m
a Torq
ue is e
qual Ze
ro 0
N
.m (no loa
d
)
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Artific
i
al Optimal Fuz
z
y
Control Strategy for El
evato
r
Dri
v
e System
by Using
…
(Salam
Waley)
479
(a) Spe
e
d
(b) T
o
rq
ue
(c) Pha
s
e cu
rrent
Figure 16. Speed, Torque
& Current
Re
spo
n
se, n = (0,-150
0
,0)rad
/ s &TL = 0N.
m
a Torq
ue is
equal Ze
ro 0
N
.m (no lo
ad)
(a) Spe
e
d
(b) T
o
rq
ue
(c) Pha
s
e cu
rrent
Figure 17. Speed, Torque
& Current Re
spo
n
se
, n = (0,-150
0 ,0)ra
d
/ s &TL = 10N.m
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