TELKOM
NIKA Indonesia
n
Journal of
Electrical En
gineering
Vol.12, No.5, May 2014, pp
. 3392 ~ 33
9
8
DOI: http://dx.doi.org/10.11591/telkomni
ka.v12i5.4950
3392
Re
cei
v
ed O
c
t
ober 2
5
, 201
3; Revi
se
d Decem
b
e
r
3, 2013; Accepte
d
De
cem
ber
22, 2013
Adaptive Control for Brushless DC Motor Based on
Fuzzy Inference
Lei Jin-li
Dep
a
rtment of Electron
ics & Electric Eng
i
ne
e
r
ing,
Bao
ji Un
iv
ersit
y
of Arts & Scienc
e, Baoji,
Chin
a
*Corres
p
o
ndi
n
g
author, e-ma
i
l
: leiji
nli
@
so
hu.
com
A
b
st
r
a
ct
Due to th
e n
o
n
lin
ear
ity of br
ushl
ess dir
e
ct current (B
LDC) m
o
tor, it is
diffi
cult to obtain satisfied
control c
haract
e
ristics usi
ng p
r
oporti
ona
l inte
gral d
e
riv
a
tive
(PID) controll
er
. A novel a
d
a
p
tive fu
zz
y
contr
o
l
meth
od b
a
se
d
on hi
gh p
e
rfor
ma
nce sp
ee
d control is pr
op
osed i
n
this p
a
per, w
h
ich co
mb
in
es an a
d
a
p
tive
para
m
eter adj
ustme
n
t mech
anis
m
w
i
th fu
z
z
y
co
ntroll
er
to solve the pr
o
b
le
ms of
no
n-li
near
ity, para
m
ete
r
variati
ons a
n
d
loa
d
exc
u
rsio
ns that occ
u
r
in t
he
B
L
D
C
motor driv
e
sy
stem.
T
he ad
aptive par
ame
t
er
adj
ustment
me
chan
is
m can
g
i
ve b
e
tter qu
a
n
ti
z
a
tio
n
a
nd p
r
oporti
on facto
r
s of the fu
zz
y
control
l
er w
h
e
n
there ar
e var
i
at
ions
in
motor p
a
ra
meters
or l
o
ad, h
ence t
he f
u
zz
y
contro
l ru
l
e
s are
cha
n
g
e
d
. T
he a
d
a
p
tiv
e
fu
zz
y
co
ntrol s
ystem is si
mu
l
a
ted in
matl
ab
w
i
th
the changes of motor p
a
ra
mete
rs a
n
d
load, the co
ntrol
perfor
m
a
n
ce
of the traditi
o
nal PID c
ontr
o
ller
is
co
mp
ared w
i
th the
ada
ptive fu
zz
y
co
ntrol
l
er.
T
he
comparis
on
re
sults i
ndic
a
te t
hat the
a
daptiv
e fu
z
z
y
c
ontrol
system h
a
s st
rong
er ro
bust
and
self-a
da
pti
v
e
abil
i
ty, faster r
e
spo
n
se
time,
and
z
e
r
o
ov
ers
hoot
an
d
ste
a
d
y
state
error,
w
h
ich ca
n s
a
ti
sfy the re
qu
est of
the BLDC
m
o
t
o
r control system
.
Ke
y
w
ords
:
br
ushl
ess DC
mo
tor, adaptive c
ontro
l, fu
zz
y c
o
ntrol, simul
a
tio
n
Copy
right
©
2014 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
With the dev
elopme
n
t of power ele
c
tronics
techniq
ue, BLDC m
o
tor ha
s bee
n widely
use
d
in moti
on co
ntrol a
p
p
licatio
ns
su
ch as a
e
ro
sp
a
c
e, ele
c
tri
c
vehicl
es, robot
ics, nu
meri
ca
lly
controlled m
a
chin
e tools,
medical eq
ui
pments,
a
nd
so on. Sin
c
e
the BLDC m
o
tor do
esn’t use
the phy
sical
conta
c
t b
e
tween th
e m
e
chani
cal
bru
s
h
and
commut
a
tor, in
stea
d, it is ele
c
tro
n
i
c
ally
comm
utator;
the BLDC
m
o
tor h
a
s the
advanta
g
e
s
of high
er
po
wer/
weig
ht, h
i
gher efficie
n
c
y,
highe
r reliabil
i
ty, and small
size, etc. Bu
t compa
r
e
d
with the DC m
o
tors, the B
L
DC m
o
tors yi
eld
a more
compl
i
cated
control
proble
m
.
PID cont
rolle
r is usually e
m
ployed in a
BLDC
mo
t
o
r
cont
r
o
l sy
st
e
m
bec
au
se it
is sim
p
le
in algorith
m
and realization. But PID control
pa
ra
meters dep
e
nd on a
c
curate mathema
t
ical
model of the
controlled
system, and it isn’t chan
ge au
tomatically when the
op
erating co
nditio
n
of
controlled
system ch
ang
e
s
su
ch a
s
di
sturb
a
n
c
e
s
a
nd load chan
ges, be
side
s,
it is difficulty to
obtain a sufficient hig
h
pe
rforman
c
e in t
he nonli
nea
r
system u
s
in
g
PID controlle
r. It is, howev
er
,
kno
w
n that th
e BLDC m
o
to
r is a no
nline
a
r sy
stem wit
h
multi-varia
b
l
es. It is very difficult to gain
an a
ccu
rate
mathemati
c
al
model of th
e BLDC mot
o
r. Furth
e
rm
ore, some p
a
ram
e
ters of
the
motor
are u
s
ually time-va
r
ying an
d u
n
certain. T
h
u
s
the PID
co
ntrolle
r fail
s to
obtain
optim
al
perfo
rman
ce
with the req
u
i
r
eme
n
ts that BLDC m
o
tor
control syste
m
shoul
d be
more a
c
curate,
fas
t
er, and more effic
i
ent [1-5].
Re
cently, ma
ny new control method
s a
r
e ad
opted t
o
the BLDC motor control
system
su
ch
as fu
zzy cont
rol, a
d
aptive control
,
neu
ral
co
ntrol, a
nd
slidi
ng m
odel
co
ntrol, et
c [6
-10].
Referen
c
e [7
] com
b
ine
s
a
model
reference a
daptiv
e sy
stem
wit
h
a
r
tificial n
e
ural
network
to
solve the
pro
b
lems of no
nl
inear,
param
eter vari
ation
s
an
d lo
ad e
x
cursion
s
th
a
t
occur i
n
BL
DC
motor
drive
systems. In [8
], in ord
e
r to
achi
ev
e hig
h
perfo
rman
ce
spe
ed trackin
g
, an a
daptiv
e
backsteppi
ng
controller i
s
de
sign
ed
to obtain th
e referen
c
e
voltage for the pul
se wi
dth
modulatio
n in
the BLDC m
o
tor co
ntrol
system. In [9], XIA Chang-li
ang et al. prese
n
t an aut
o
-
tuning metho
d
for fuzzy logic controll
er
based
on g
e
n
e
tic algo
rithm
for the BLDCM control.
Both fuzzy co
ntrol a
nd a
d
a
p
tive cont
rol
are
nonli
nea
r
control meth
o
d
s, h
a
ve adv
antage
s
of the robust, self-adapt
ive ability, simpl
e
structure, and
so
on, they can be a
very good deal
with un
ce
rtai
nty, non-lin
ea
rity, time vari
ability and
co
upling
of sy
stem, and
they
are ve
ry suita
b
le
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Adaptive
Con
t
rol for Bru
s
hl
ess DC Motor Based on F
u
zzy Inferen
c
e
(Lei Jin
-
li)
3393
for the BLDC motor syste
m
[11-17]. In this pape
r,
it is pro
p
o
s
ed fo
r BLDC moto
r system that an
adaptive fuzzy cont
rolle
r combi
n
e
s
fuzzy cont
ro
ll
er an
d ada
p
t
ive controll
e
r
, the pro
p
o
s
ed
controlle
r can
adju
s
t the
controlle
r p
a
ra
meters on
-lin
e, kee
p
s sim
p
le st
ru
cture,
and
ha
s rob
u
st
perfo
rman
ce
again
s
t dist
urba
nces
an
d load vari
ations. Th
e p
e
rform
a
n
c
e
of the prop
o
s
ed
controlle
r wa
s compa
r
ed
with PID con
t
roller u
s
in
g
MATLAB SIMULINK
soft
ware. Simula
tion
results prove
that the a
daptive fuzzy cont
rolle
r sho
w
s much
higher stati
c
and dyna
mic
perfo
rman
ce
than PID cont
rolle
r.
2. Mathema
t
i
c
Model of BLDC M
o
tor
A th
r
e
e-
ph
ase
BLD
C
mo
to
r
is
a
d
o
p
t
ed
in th
is p
ape
r
,
th
e BLDC
mo
to
r co
ns
is
ts
o
f
a
perm
ane
nt magnet roto
r
and
stato
r
windi
ng
s,
wh
ich
are
si
nu
soid
ally dist
ri
buted i
n
the
“Y”
con
n
e
c
tion, a
nd the curren
t always p
a
sses throug
h two pha
se
wind
ings.
Unde
r t
he a
s
sumptio
n
of linea
r m
a
g
netic
structu
r
e, the th
ree
-
p
hase
st
ator
winding
s are
completely
e
q
ual,
the self
- and
mutual indu
ct
ances a
r
e
co
nstant, and t
he effect
of alveolu
s
, co
mmutation a
nd the arm
a
ture
rea
c
tion
are i
gnored, th
e
mathemati
c
al
mod
e
l of
BL
DC moto
r
ca
n be
d
e
scri
be
d by th
e follo
wing
equatio
ns [18
]
:
a
aa
a
a
a
di
uR
i
L
e
dt
(1)
b
bb
b
b
b
di
uR
i
L
e
dt
(2)
c
cc
c
c
c
di
uR
i
L
e
dt
(3)
2
aa
b
b
c
c
e
ei
e
i
e
i
P
T
(4)
eL
d
TT
J
B
dt
(5)
Whe
r
e
a
u
,
b
u
,
c
u
are
the pe
r p
h
a
s
e voltage
of
pha
se a,
b a
nd c resp
ecti
vely,
a
i
,
b
i
,
c
i
are th
e
per
pha
se
cu
rre
nt of p
h
a
s
e
a
,
b
an
d
c
res
p
ec
tively,
a
R
,
b
R
,
c
R
are
the
pe
r p
hase resi
stan
ce
of
pha
se
a
,
b
a
nd
c
r
e
spec
tively,
a
L
,
b
L
,
c
L
are t
he p
e
r
pha
se ind
u
cta
n
ce
of ph
ase
a
,
b
an
d
c
r
e
spec
tively,
a
e
,
b
e
,
c
e
are the
p
e
r p
h
a
s
e
ba
ck ele
c
tro
m
o
t
ive force
of
pha
se
a
,
b
and
c
r
e
spec
tively,
is
the roto
r spe
ed,
e
T
and
L
T
are
ele
c
tro
m
agneti
c
torque d
e
velop
ed by th
e
motor an
d loa
d
torque,
J
an
d
B
are inerti
a and frictio
n
coeffici
ents.
Thro
ugh a
n
a
l
yzed the m
a
thematical model of
the
BLDC moto
r, we can o
b
tain the
dynamic
stru
cture
[19
-
20],
as
sho
w
n
in
Figure 1.
In t
he Fi
gure 1,
U
d
is di
re
ct
current voltag
e,
R
is stato
r
wi
nd
ing re
si
stan
ce and
R=
R
a
=R
b
=R
c
,
L
is
stator win
d
ing
indu
ctan
ce a
nd
L=
L
a
=L
b
=L
c
,
C
T
is
torque cons
tant,
C
e
is voltage con
s
tant,
GD
2
is flywheel mo
m
ent of inertia.
Figure 1. The
BLDC Moto
r
Dynami
c
Stru
cture
1
1
1
s
T
L
L
R
s
GD
2
375
T
C
e
C
d
U
L
T
)
(
s
n
Evaluation Warning : The document was created with Spire.PDF for Python.
ISSN: 23
02-4
046
TELKOM
NI
KA
Vol. 12, No. 5, May 2014: 3392 – 33
98
3394
The tra
n
sfo
r
m function
of BLDC
motor
control
sy
ste
m
can
be de
ri
ved from the
Figure 1,
as sho
w
n in
Equation (6),
22
2
2
22
1
()
()
(
)
11
37
5
3
75
37
5
3
75
ee
T
e
T
dL
eT
eT
LR
s
CC
C
C
C
ns
U
s
T
s
LGD
R
GD
LGD
R
GD
ss
ss
CC
CC
(6)
3. Adap
tiv
e
Fuzz
y
Controller of BL
DC Moto
r
3.1. BLDC M
o
tor Contr
o
l Sy
stem Structure
To improve the accu
ra
cy and dyna
mic perfo
rman
ce
, the BLDC
motor control
system
adopt
s d
oubl
e cl
osed lo
o
p
control
sch
e
me of
sp
ee
d an
d
curren
t, the co
ntrol
system
blo
c
k i
s
sho
w
n
in
Fig
u
re
2. O
u
ter l
oop i
s
speed
loop
to
adju
s
t the
spee
d
usin
g a
daptiv
e fuzzy
strat
e
gy,
and inn
e
r loo
p
is cu
rrent lo
op to dire
ctly
control cu
rren
t using PI con
t
roller.
Figure 2. Con
t
rol System Structu
r
e of the BLDC M
o
to
r
Adaptive fuzzy co
ntrolle
r is co
nsi
s
ted
of
fuzzy co
ntrolle
r and
para
m
eters t
uner
of
prop
ortio
n
a
nd qua
ntizat
ion facto
r
s.
Paramete
rs tuner
conti
nuou
sly adju
s
ts o
n
-lin
e
the
prop
ortio
n
an
d qua
ntizatio
n facto
r
s
of fuzzy co
ntroll
er ba
se
d on
spe
ed e
r
ror and its
rate
of
cha
nge, the
fuzzy control rule
s a
r
e
chan
ged
to
o, and the
control varia
b
le are adju
s
te
d
automatically. There
b
y, the prop
osed BL
DC m
o
to
r co
ntrol
sy
stem has
g
ood stat
ic
and dynam
ic
perfo
rman
ce,
self-a
daptiv
e ability, and
stron
g
e
r
ro
b
u
stne
ss wh
e
n
the BLDC
motor o
perating
con
d
ition cha
nge
s su
ch a
s
disturb
a
n
c
e
s
and load
cha
nge
s, and so on.
3.2. Design
of Fuzz
y
Controller
The fu
zzy
co
ntrolle
r i
s
sel
e
cted
a t
w
o-d
i
mens
i
onal
st
ructu
r
e
with
a
dual
-inp
ut an
d si
ngle
output, the in
puts
of ada
ptive fuzzy con
t
roller are
sp
eed
error
e
a
nd
e
r
ror ch
a
nge rate
ec
, t
he
output is refe
rence cu
rrent
r
i
.
The fu
zzy
do
main of i
nput
s (E, E
C
)
are
taken
a
s
[-6,
6], and the
o
u
tput (Ir) fu
zzy domain
are ta
ke
n a
s
[-7, 7]. Both i
nputs fuzzy
subsets
of
ling
u
istic
varia
b
le
and output’
s
are sel
e
cte
d
as
{NB, NM,
NS
, ZE, PS, PM
, PB}. For the s
a
k
e
of s
i
mplic
ity, the
membership
func
tion
of E, EC
and Ir are taken trigon
omet
ric fun
c
tion, a
s
sh
own in Figure 3.
(a) E and E
C
membe
r
ship functio
n
(b) Ir members
h
ip func
tion
Figure 3. Membershi
p
Fun
c
tion of E, EC and Ir
-6
-4
-2
0
2
4
6
0
0.
2
0.
4
0.
6
0.
8
1
D
egr
ee of
m
e
m
ber
s
h
i
p
NB
NM
N
S
Z
E
P
S
P
M
P
B
-6
-4
-2
0
2
4
6
0
0.
2
0.
4
0.
6
0.
8
1
D
egr
ee
of
m
e
m
ber
s
h
i
p
NB
NM
NS
Z
E
P
S
P
M
P
B
Evaluation Warning : The document was created with Spire.PDF for Python.
TELKOM
NIKA
ISSN:
2302-4
046
Adaptive
Con
t
rol for Bru
s
hl
ess DC Motor Based on F
u
zzy Inferen
c
e
(Lei Jin
-
li)
3395
Fuzzy co
ntrol rule
s are
a seri
es o
f
fu
zzy con
d
itional state
m
ents ba
se
d on the
kno
w
le
dge a
nd experi
e
n
c
e of an experienced ope
ra
t
o
r as
well as
an expert. In the BLDC mo
tor
control
syste
m
, the fu
zzy
controlle
r pl
a
y
s a
role
that
sy
st
em
out
pu
t
is in
f
a
st
re
s
pon
se t
o
sy
st
em
input and
un
certain di
sturb
ances a
s
soo
n
as p
o
ss
ibl
e
. Based on th
e prin
cipl
es a
nd the previo
us
experie
nce in
the BLDC m
o
tor co
ntrol, the fuzzy cont
rol rul
e
s a
r
e o
b
tained, a
s
shown in Tabl
e 1.
Table 1. Fu
zzy Control Rul
e
s
Ir
EC
NB NM
NS
ZE
PS
PM
PB
E
NB
PB PB
PB
PB
PM
PS
ZE
NM
PB
PB
PB PM
PM PS
ZE
NS
PB
PB PM
PM PS
ZE
NS
ZE
PB PM
PS
ZE
NS
NM
NB
PS
PM PS
ZE
NS
NM
NM
NB
PM
PS
ZE
NS
NM NB
NB NB
PB
ZE
NS NM
NB
NB
NB NB
The M
a
mda
n
i
infere
nce re
aso
n
ing
algo
rithm is
ado
pted a
s
fu
zzy inferen
c
e
met
hod i
n
this pap
er. A
c
cordi
ng to fuzzy cont
rol
rule
s in Tabl
e 1, a fuzzy
set of output
can be
obtai
ned
usin
g Mamd
a
n
i infere
nce reasonin
g
alg
o
rithm w
hen
inputs a
r
e gi
ven. This fu
zzy set of out
put
must b
e
defu
zzifi
cation.
Defuzzificatio
n
of out
put info
rmation
ado
p
t
s wei
ghted
a
v
erage
meth
od,
the formula i
s
:
n
ii
i1
n
i
i1
Ir
μ
Kr
Ir
μ
Kr
(7)
3.3. Design
of Parame
ter
s
Adap
tiv
e
T
uning
Fuzzy control
l
er i
s
a
controller
usi
ng fu
zzy
li
ngui
stic
variable
s
. A
m
ong
whi
c
h,
the input
variable
s
do
main i
s
tra
n
sformed i
n
to t
he fuzz
y d
o
m
ain by the
quanti
z
ation f
a
ctors
(ke, kec),
while
the
out
put do
main
is tran
sformed
into the
fu
zzy dom
ain
by the p
r
op
ortio
n
facto
r
(kIr).
The
values of the
prop
ortion a
nd qua
ntizati
on facto
r
s aff
e
ct the stati
c
and dynami
c
performan
ce
of
the BLDC mo
tor control
sy
stem, which
will be
adju
s
ted by ad
aptive tuner
ac
co
rding to the
erro
r
and di
sturb
a
n
c
e of syste
m
con
s
e
que
ntly.
As
e
and
ec
are
all la
rge
r
,
the mai
n
ta
sk of
adaptive
tuner i
s
to
eli
m
inate e
r
ror.
Thus the
siz
e
of
ke
and
ke
c
sh
ould be
smalle
r
to redu
ce
influe
nce of
e
a
nd
ec
, an
d the v
a
lue of
kI
r
sh
ould
be big
ger to
decrea
s
e
the
respon
se
time
and
en
su
re
stability of control
system
. When
e
an
d
ec
are all
small
e
r, the co
ntrol
system i
s
cl
o
s
e to
st
eady
state. The
system'
s
main t
a
sk is to
stabi
lize
as q
u
ickly a
s
possibl
e. Th
e wei
ght of
ke
and
ke
c
sh
ould b
e
big
g
e
r
to in
cre
a
se the influe
nce
of
e
and
ec
.
an
d
the val
ue
o
f
kI
r
should
be
small
e
r t
o
avoid
ove
r
-sh
oot.
Com
b
ined
the
ab
ove
prin
ciple
with
the BLDC
mathemati
c
model, t
he f
unctio
n
s
belo
w
is
ch
osen
as p
e
rfo
r
ma
nce
function
s of the
ke
,
kec
and
kI
r
.
(
)
(
(
),
(
)
,
(
))
L
ke
m
f
e
m
ec
m
T
m
(8)
()
(
(
)
,
()
,
(
)
)
L
k
e
cm
g
e
m
e
cm
T
m
(9)
()
(
(
)
,
()
,
(
)
)
L
kI
r
m
y
e
m
e
c
m
T
m
(10)
The value of
ke,
ke
c
an
d
kI
r
ca
n be
continuo
usly a
d
juste
d
by the perfo
rma
n
ce index
function
J whi
c
h can be ex
pre
s
sed a
s
:
22
()
()
()
()
()
()
L
JE
P
m
e
m
S
m
e
c
m
Q
m
T
m
(11)
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Vol. 12, No. 5, May 2014: 3392 – 33
98
3396
Where as
sume that:
0
()
0
()
0
()
L
J
em
J
ec
m
J
Tm
(12)
Cal
c
ulate Eq
uation (1
2), P(m), S(m
)
, Q(m) wo
uld be
obtaine
d, and
ke,
ke
c
and
kI
r
can
be expre
s
sed
as Equation
(13
)
.
1
1
1
()
()
()
()
()
()
()
()
()
m
i
m
i
m
i
Pm
ke
m
Pm
Sm
kec
m
Sm
Qm
kI
r
m
Qm
(13)
Equation
(1
3) sh
ows th
at t
he valu
e of
ke,
ke
c
and
kI
r ca
n b
e
cha
n
ged
co
ntinuo
usly a
nd
adaptively according to the
erro
r an
d disturban
ce
of system, so the
adaptive fuzzy cont
rolle
r ca
n
easily ad
apt to the nonlin
e
a
r BLDC mot
o
r co
ntrol
system.
4. Simulation and Analy
s
is
To test the
perfo
rman
ce
of the adapti
v
e fuzzy
con
t
roller, we ca
rrie
d
out a serie
s
of
simulatio
n
ex
perim
ents
usi
ng Matlab/Si
mulink
softw
are. Th
e ad
a
p
tive fuzzy
controlle
r an
d
the
PID controller are sim
u
late
d re
spe
c
tively. The para
m
e
t
ers of the BL
DC moto
r a
r
e
:
DC voltage
U
= 50
0V, rate
d
power P
=
30
00W, the
re
si
st
an
ce
of stat
or windin
g
R =
2.87
5
Ω
,
in
d
u
ct
an
ce
s of
t
h
e
stator L = 8.
5mH, mome
n
t
of inertia J = 0.
001
2kg.m2, rated sp
eed n = 30
0
0
r/min, numb
e
r of
pole pai
rs p
= 2.
4.1. Simulation
The
BL
DC motor cont
ro
l
system sim
u
lation
exp
e
riments
we
re
ca
rrie
d
o
n
different
operating con
d
itions
su
ch as chan
ge in
refe
re
nce sp
eed, ch
ange
in load torq
u
e
and ch
ang
e in
moment
of in
ertia, the
re
spon
se
cu
rves of the a
dapti
v
e fuzzy co
ntrolle
r an
d the
PID controll
er
were sh
own in Figure 4-Fi
gure 7.
Figure
4
sh
ows
the
spe
ed re
spo
n
se
co
mp
a
r
ed
adaptive fu
zzy controller with
PID
controlle
r for
a ste
p
cha
n
g
e
in
refe
ren
c
e
sp
eed. It
can
be
se
en th
at
the BLDC mo
tor
system
wit
h
adaptive fuzzy controll
er takes 40m
s
to reach steady stat
e with
zero overshoot
and steady state
error,
while
t
he PID cont
rol
system
t
a
ke
s
60m
s t
o
rea
c
h
stea
dy state
wit
h
a
pe
rcent
age
overshoot of 9% and ze
ro
steady
state error.
Figure 5 shows the
speed response of adapt
ive fuzzy controller and
PID cont
rol system
for a ste
p
cha
nge in lo
ad to
rque. It ca
n b
e
se
en
that th
e BLDC moto
r syst
em with
adaptive fu
zzy
controlle
r takes 40m
s to reach stea
dy state with zero overshoot
and ste
ady st
ate error, and
the
PID control
system ta
ke
s
70ms to
rea
c
h ste
ady
st
ate with
a
pe
rcentage
ov
ershoot
of 7.5% an
d
zero stea
dy state erro
r.
Figure
6
sh
ows
the
spe
ed re
spo
n
se
co
mp
a
r
ed
adaptive fu
zzy controller with
PID
controlle
r for
a step
cha
n
g
e
in refe
ren
c
e sp
eed a
s
J
is 0.00
12Kg.
m2. As sho
w
n in the Fig
u
re 6,
the ada
ptive fuzzy co
ntrol
system ta
ke
s 40m
s to
rea
c
h steady sta
t
e
with zero
o
v
ersh
oot stea
dy
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TELKOM
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ISSN:
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Adaptive
Con
t
rol for Bru
s
hl
ess DC Motor Based on F
u
zzy Inferen
c
e
(Lei Jin
-
li)
3397
state error,
and the sy
stem with
PID controll
er ta
ke
s 80m
s to reach stea
d
y
state with a
percenta
ge o
v
ersh
oot of 6%
and ze
ro st
eady state error.
Figure 7
sho
w
s th
e spee
d
respon
se
usi
ng ad
aptiv
e fuzzy co
ntrolle
r and PI
D co
ntrolle
r for a
step
cha
nge i
n
referen
c
e
spee
d
whe
n
J i
s
0.
002Kg.m2. It
can
be
se
en
that the BL
DC moto
r
syst
em
with ad
aptive
fuzzy
co
ntrol
l
er ta
ke
s 40
m
s
to rea
c
h ste
ady
state wit
h
ze
ro
overshoot
an
d ste
a
d
y
state e
rro
r, a
nd the PID
contro
l
sy
st
em
t
a
ke
s 9
0
ms
t
o
rea
c
h
stea
dy state
with
a pe
rcentag
e
overshoot of 10% and zero steady stat
e error.
Figure 4. Speed Re
sp
on
se
with Step Ch
ange
in Referen
c
e
Speed
Figure 5. Speed Re
sp
on
se
with Step Ch
ange
in Load To
rq
ue
Figure 6. Speed Re
sp
on
se
with Step Ch
ange
in Referen
c
e
Speed (J=0.0
012
kg.m2
)
Figure 7. Speed Re
sp
on
se
with Step Ch
ange
in Referen
c
e
Speed (J=0.0
02kg.m2)
4.2. Simulation Res
u
lts Analy
s
is
Table 2. Perf
ormance Parameters
of Adaptive Fuzzy and PID Controller
Simulation experi
m
ent
Response time(s)
Overshoot
(%)
Changes in oper
ating conditions
Controller
Change in ref
e
re
nce speed
Adaptive fuzzy
0.04
0
PID 0.06
9
Change in load t
o
rque
Adaptive fuzzy
0.04
0
PID 0.07
7.5
J
=0.0012 Kg.m
2
Adaptive fuzzy
0.04
0
PID 0.08
6
J
=0.002 Kg.m
2
Adaptive fuzzy
0.04
0
PID 0.09
10
The sim
u
latio
n
results of the BLD
C
mo
to
r cont
rol sy
stem co
mpa
r
ed the adapti
v
e fuzzy
controlle
r wit
h
the PID co
ntrolle
r are
shown in T
abl
e 2. The sim
u
lation re
sult
s cle
a
rly sho
w
that
the steady a
nd dynami
c
perfo
rman
ce
of adaptiv
e fuzzy co
ntrollers is bett
e
r than the
PID
controlle
r d
u
ring referen
c
e
sp
eed, l
oad
torq
ue
and
inertia
ch
ang
es. T
he BL
DC m
o
tor
wit
h
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Vol. 12, No. 5, May 2014: 3392 – 33
98
3398
adaptive fuzzy controller is able to
respond with sm
aller response
time, zero st
eady state and
overshoot.
5. Conclusio
n
In this pape
r, we con
s
id
e
r
the ada
ptive
fuzzy control algorith
m
for the BLDC motor
system with para
m
eters’ uncertainty
a
nd
no
n
linea
ri
ty, and the fuzzy co
ntroll
er an
d ad
apt
ive
controlle
r are nonlin
ear in
n
a
ture. It is pro
posed t
hat fu
zzy
control a
nd ada
ptive a
l
gorithm
can
be
combi
ned b
a
s
ed o
n
the BLDC m
o
tor mathematics model. F
r
o
m
the comp
arison si
mula
tion
results
of ad
aptive fuzzy
controlle
r an
d PID
cont
rol
l
er, fou
r
mai
n
co
ntributio
ns of this
re
se
a
r
ch
a
r
e
co
nc
lu
ded
:
(1)
The ada
ptive fuzzy controller ca
n ac
hiev
e good
stead
y and dynami
c
perfo
rma
n
ce;
(2)
The ad
aptive
fuzzy
cont
roller h
a
s
stronge
r robu
st
and
self-ad
aptive wh
en
the motor
para
m
eters chang
es a
nd l
oad di
sturb
s
;
(3)
The ada
ptive fuzzy controller is ea
sy to impleme
n
t;
(4)
The pe
rform
a
nce of the ad
aptive fuzzy c
ontrolle
r is
su
perio
r to the PID controller.
In
sum
m
ary, the
ada
ptive fuzzy co
ntroll
er
can
conqu
er the
proble
m
su
ch
a
s
n
online
a
r
and p
a
ra
met
e
r vari
ety of the BLDC mo
tor, and i
s
a
good
pro
p
o
s
al for the BL
DC m
o
tor
co
ntrol
sy
st
em.
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ces
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an B, S
i
ngh M.
A
d
a
p
ti
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agi
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o
rith
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o
ft
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u
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e
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ra
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zz
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ng-j
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w
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oo-Yon
g
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g
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zz
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ML W
ang, GH
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din
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i
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heng
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hen
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h
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z
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a
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ontro
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ons
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S
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u
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ada
ptive activ
e
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rol
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o
tic s
ystem
in perm
ane
nt magn
et
s
y
nc
hro
nous
motor
w
i
t
h
p
a
r
a
meters pertur
batio
n.
Jo
urna
l
of H
uaq
ia
o u
n
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