Inter
national
J
our
nal
of
Electrical
and
Computer
Engineering
(IJECE)
V
ol.
9,
No.
6,
December
2019,
pp.
5304
5311
ISSN:
2088-8708,
DOI:
10.11591/ijece.v9i6.pp5304-5311
r
5304
Fuzzy-PID
contr
oller
f
or
an
ener
gy
efficient
personal
v
ehicle:
T
w
o-wheel
electric
skateboard
Bambang
Sumantri
1
,
Ek
o
Henfri
Binugroho
2
,
Ilham
Mandala
Putra
3
,
Rika
Rokhana
4
1,3,4
Electrical
Department,
Politeknik
Elektronika
Ne
geri
Surabaya
(PENS),
Indonesia
2
Department
of
Mechanical
and
Ener
gy
,
PENS,
Indonesia
Article
Inf
o
Article
history:
Recei
v
ed
F
ab
27,
2019
Re
vised
Jul
21,2019
Accepted
Jul
29,
2019
K
eyw
ords:
Personal
v
ehicle
T
w
o-wheeled
electric
skateboard
Fuzzy-PID
Balancing
control
Ener
gy
ef
ficienc
y
ABSTRA
CT
The
tw
o-wheeled
electric
skateboard
(TWS)
is
designed
for
a
personal
v
ehicle.
A
Fuzzy-PID
control
strate
gy
is
designed
and
implemented
for
controlling
its
motion.
Basically
,
motions
control
of
the
TWS
is
performed
by
balancing
the
pitch
position
of
the
TWS.
Performance
of
the
designed
controller
is
demonstrated
e
xperimentally
.
The
Fuzzy
algorithm
updates
the
PID
g
ains
and
therefore
it
can
handle
the
changing
of
the
TWS
load.
Contrib
ution
of
Fuzzy-PID
in
reducing
the
electric
ener
gy
consumption,
which
is
an
important
issue
in
electrical
system,
is
also
e
v
aluated.
The
Fuzzy-PID
successes
to
reduce
the
electric
ener
gy
consumption
of
the
TWS
compared
to
the
con-
v
entional
PID.
Copyright
c
2019
Institute
of
Advanced
Engineering
and
Science
.
All
rights
r
eserved.
Corresponding
A
uthor:
Bambang
Sumantri,
Politeknik
Elektronika
Ne
geri
Surabaya,
Kampus
PENS,
Jalan
Raya
ITS,
K
eputih-Suk
olilo,
Surabaya,
Indonesia,
60111.
T
el:
+62-31-5947280
Email:
bambang@pens.ac.id
1.
INTR
ODUCTION
No
w
adays,
due
to
their
acti
vity
,
mobility
of
people
in
an
area
become
f
aster
.
The
y
need
to
mo
v
e
from
one
to
the
other
place
in
an
area
rapidly
,
personally
and
fle
xibly
.
Therefore,
a
Simple
Personal
V
ehicle
(SPV)
for
transporting
the
person
is
needed,
such
as:
traditional
SPV
(roller
-skates,
skateboard,
snak
e-board,
or
scooter)
and
modern
SPV
(one-wheel,
se
gw
ay
,
ho
v
erboard,
or
motorized
skateboard).
T
o
ride
the
traditional
SPV
,
we
need
more
ef
for
ts
and
skill
compared
to
the
modern
SPV
[1].
The
modern
SPV
utilizes
electric
motorized
wheel
including
its
motion
control.
Therefore,
by
pro
viding
an
e
xcellent
motion
control
that
considering
smooth
response
and
safety
,
less
skill
of
the
rider
is
needed
for
operating
the
SPV
.
Basically
,
the
modern
SPV
can
be
considered
as
a
self
balancing
robot
that
beha
v
es
resembling
the
in-
v
erted
pendulum.
Research
on
self
balancing
robot,
especially
in
controller
de
v
elopment,
g
ains
a
lot
of
attention
o
v
er
the
last
decade.
Model
and
non-model
based
controller
ha
v
e
been
designed
by
the
researchers.
Some
model-based
control
strate
gies
ha
v
e
been
proposed,
such
as
LQR
[3-6],
or
sliding
mode
control
[9,
10].
Ho
we
v
er
,
in
model-based
control
strate
gy
,
dynamics
of
the
system
should
be
pro
vided
which
is
not
easy
to
obtain.
Combination
of
Proportional
(P),
Inte
gral
(I),
and
Dif
ferential
(D)
control
method,
as
a
common
control
strate
gy
,
has
also
been
considered
by
researchers
for
stabilizing
the
self
balancing
robot
[11-17].
PID
control
strate
gy
is
v
ery
common
due
to
its
simplicity
in
implementation
e
v
en
without
kno
wing
the
model
of
the
system.
Ho
we
v
er
,
the
control
g
ains
of
the
PID
are
tuned
in
certain
condition.
Therefore,
if
the
controlled
system
has
v
arying
parameter
or
interfered
by
unkno
wn
disturbance,
the
PID
controller
cannot
guaranty
the
J
ournal
homepage:
http://iaescor
e
.com/journals/inde
x.php/IJECE
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
5305
performance
and
stability
of
the
system.
Ho
we
v
er
,
by
updating
the
g
ains
of
PID
continuously
follo
wing
chang-
ing
of
the
system
condition,
a
good
control
performance
can
be
obtained.
On-line
updating
of
P
ID
g
ains
can
be
performed
by
implementing
Fuzzy
algorithm
as
in
[18-22].
The
adv
antage
of
using
Fuzzy
algorithm
is
we
do
not
need
prior
kno
wledge
of
systems
model.
Therefore,
comple
xity
in
deri
ving
mathematical
model
of
the
system
can
be
a
v
oided.
In
this
w
ork,
a
modern
SPV
is
designed
by
resembling
a
traditional
skateboard
b
ut
using
tw
o
wheels,
called
T
w
o-wheel
skateboard
(TWS).
Therefore,
self
balancing
robot
can
be
considered
for
controlling
motion
of
the
TWS.
Fuzzy-PID
method
is
utilized
in
this
w
ork.
Fuzzy
algorithm
is
utilized
for
updating
proportional
(
K
p
)
and
inte
gral
(
K
i
)
g
ains
instead
of
all
three
PID
g
ains.
Performance
of
the
designed
system
is
demonstrated
e
xperimentally
and
compared
to
the
con
v
entional
PID.
Since
ener
gy
consumption
is
also
an
important
issue
in
electrical
TWS,
then
we
e
v
aluate
the
contrib
ution
of
Fuzzy-PID
in
reducing
the
ener
gy
consumption
compared
to
the
con
v
entional
PID.
By
v
arying
K
p
and
K
i
,
the
PID
controller
adapts
to
parameters
changing
of
the
TWS,
such
as
load
or
mass.
2.
SYSTEM
DESIGN
2.1.
T
w
o-wheel
skateboard
The
TWS
is
b
uilt
by
considering
the
v
ariation
of
user
mass
and
their
con
v
enience.
A
complete
ap-
pearance
of
the
TWS-design
and
electronics
system
place
ment
are
sho
wn
in
Figure
1(a).
The
system
block
diagram
is
gi
v
en
in
Figure
1(b).
An
ARM
Corte
x
STM32F407V
G
is
utilized
as
the
main
controller
board.
T
w
o
Brushless
DC-Motors
(BLDC)
with
hall-ef
fect
sensor
are
used
for
motion
actuator
as
a
couple
of
dif
ferential
motor
dri
v
e.
The
diagram
of
motor
dri
v
er
is
sho
wn
in
Figure
2(a).
The
v
oltage
and
current
of
each
motor
is
also
measured
by
the
ADC
for
measuring
the
ener
gy
consumption.
The
IMU
sensor
MPU-6050
is
utilized
for
measuring
the
pitch
angle
(
).
The
schematics
of
MPU-6050
is
sho
wn
in
Figure
2(b).
The
tw
o
load-cell
located
at
left
and
right
side
are
used
for
steering
the
TWS
motion,
and
the
schematics
is
gi
v
en
in
Figure
2(c).
In
addition,
a
bluetooth
module
HC-06
is
used
for
monitoring
TWS
condition
via
PC.
(a)
(b)
Figure
1.
(a)
Complete
vie
w
and
Electronics
system
placement
of
TWS,
(b)
System
block
diagram
(a)
(b)
(c)
Figure
2.
(a)
Diagram
of
motors
dri
v
er
,
(b)
Pitch
sensor
system
using
MPU-6050,
(c)
Schematic
of
load-cell
for
steering
the
TWS
Fuzzy-PID
contr
oller
for
an
ener
gy
ef
ficient
per
sonal
vehicle:
T
wo-wheel...
(Bambang
Sumantri)
Evaluation Warning : The document was created with Spire.PDF for Python.
5306
r
ISSN:
2088-8708
2.2.
Motion
contr
ol
Basically
,
the
motion
of
TWS
in
forw
ard
or
backw
ard
is
dri
v
en
by
the
changing
of
its
pitch
angle
(
)
that
figuring
the
center
of
mass
(CoM)
position.
This
changing
is
dri
v
en
by
the
TWS
passenger
that
pushing
his
or
her
body
forw
ard
or
backw
ard.
If
=0
is
the
desired
set-point,
then
the
TWS
is
stabilized
and
freezing
in
its
condition.
The
v
alue
of
go
v
erns
the
BLDC
ho
w
f
ast
it
mo
v
es.
In
addition,
the
ya
wing
motion
in
lef
t
or
right
is
dri
v
en
by
the
v
alue’
s
dif
ferent
between
the
tw
o
load-cell
(
v
).
Therefore,
the
motion
of
the
TWS
occurs
due
to
balance
control
of
CoM.
A
closed-loop
PID
is
uti-
lized
for
the
balance
controller
.
Since
PID
is
cate
gorized
in
the
class
of
linear
controller
,
then
it
is
dif
ficult
to
compensate
the
change
of
systems
parameter
,
such
as
weight
of
TWS
passenger
.
Hence,
a
Fuzzy-PID
is
designed
to
handle
the
problem
of
this
parameter
changing.
Structure
of
TWS
motion
control
is
gi
v
en
in
Figure
3.
The
Fuzzy
logic
is
utilized
for
on-line
t
u
ni
ng
the
PID
parameters
and
therefore
the
controller
adapts
to
the
systems
parameter
changing.
If
passengers
with
dif
ferent
weight
ride
the
TWS
personally
,
the
controller
produces
similar
performance
and
hence
the
comfortability
when
riding
the
TWS
can
be
obtained.
Figure
3.
Control
System
block
diagram
The
equation
of
control
structure
sho
wn
in
Figure
3
is
described
as
follo
ws:
u
=
K
p
+
K
i
Z
dt
+
K
d
_
(1)
where
K
p
,
K
i
and
K
d
are
positi
v
e
constant
as
proportional,
inte
gral
and
deri
v
ati
v
e
g
ains;
and
_
is
first
deri
v
ati
v
e
of
pitch
angle.
By
considering
steering
data
from
load-cell
(
v
),
the
angular
v
elocity
for
left
and
right
wheels
can
be
calculated
as
follo
ws:
!
L
=
u
v
(2)
!
R
=
u
+
v
(3)
where
!
L
and
!
R
are
left
and
right
wheel
angular
v
elocity
,
respecti
v
ely
.
3.
FUZZY
-PID
The
control
strate
gy
gi
v
en
in
Eq.
1
is
modified
by
using
fuzzy
method
for
balancing
the
TWS,
where
=0
is
reached
in
a
smooth
response
(relati
v
ely
f
ast
and
small
oscillation).
Therefore,
only
K
p
and
K
i
are
tuned
adapti
v
ely
instead
of
all
the
three
g
ains
to
a
v
oid
the
comple
xity
in
implementation.
Hence,
and
R
dt
are
chosen
as
fuzzy
input.
Fi
v
e
membership
functions
for
each
fuzzy
input
are
designed
as
sho
wn
in
Figure
4.
In
order
to
mak
e
a
compact
and
computationally
ef
ficient,
Sugeno-fuzzy
system
is
chosen
for
the
Fuzzy-PID
controller
.
W
e
design
25
fuzzy
rules
for
adapti
v
ely
tune
K
p
and
K
i
,
as
sho
wn
in
T
able
1.
The
singleton
output
membership
functions
for
K
p
are
VS=130,
S=180,
A=190,
B=210,
VB=220;
while
for
K
i
are
VS=3.1,
S=3.6,
A=3.8,
B=4.0,
VB=4.2,
respecti
v
ely
.
The
output
fuzzy
surf
ace
for
K
p
and
K
i
are
sho
wn
in
Figure
5.
The
range
of
these
K
p
and
K
i
are
selected
based
on
the
e
xperimental
results.
Int
J
Elec
&
Comp
Eng,
V
ol.
9,
No.
6,
December
2019
:
5304
–
5311
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
5307
(a)
(b)
Figure
4.
Membership
function
of:
(a)
pitch-angle,
(b)
inte
gral-pitch-angle
T
able
1.
Fuzzy
rules
for
K
p
and
K
i
.
R
Ne
g2
Ne
g1
Nol
Pos1
Pos2
Ne
g2
VB
B
B
B
VB
Ne
g1
B
A
S
A
B
Nol
S
VS
VS
VS
S
Pos1
B
A
S
A
B
Pos2
VB
B
B
B
VB
Figure
5.
The
output
K
p
and
K
i
surf
aces
of
fuzzy
controller
4.
EXPERIMENT
AL
RESUL
TS
In
this
w
orks,
the
control
strate
gy
in
Eq.
1
is
realized
in
the
embedded
controller
STM32F407V
G.
Firstly
,
a
con
v
entional
PID
is
realized
with
dif
ferent
control
parameters
on
the
TWS.
T
w
o
combinations
of
con-
trol
parameters
are
chosen
intuit
i
v
ely
to
obtain
a
good
performance,
as
follo
ws:
1.
K
p
=
140
;
K
i
=
3
:
3
;
K
d
=
4
:
5
;
2.
K
p
=
170
;
K
i
=
3
:
3
;
K
d
=
4
:
5
.
These
controllers
are
tested
e
xperimentally
on
the
TWS
in
unloaded
and
loaded
conditions.
The
controller
must
hold
the
TWS
in
a
balance
condition
where
=
0
.
Performance
of
these
controllers
in
an
unloaded
condition
are
sho
wn
in
Figures
6.
It
is
seen
that
the
smaller
K
p
pro
vide
better
performance
in
balancing
the
TWS
by
pro
viding
smooth
response
in
and
motor
speed
as
sho
wn
in
Figure
6(a).
Ho
we
v
er
,
if
a
loaded
e
xperiment
is
performed
(Figures
7),
the
bigger
K
p
pro-
vides
better
performance
by
resulting
smaller
oscillation
in
and
motor
speed
as
sho
wn
in
Figure
7(b).
By
increasing
K
p
=
200
,
the
performance
is
impro
v
ed
as
sho
wn
in
Figure
7(c).
From
these
e
xperiments,
it
is
seen
that
the
con
v
entional
PID
cannot
deal
with
the
changing
of
load
parameter
on
TWS.
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
−10
−5
0
5
10
time (ms)
motor speed (rpm)
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
−15
−10
−5
0
5
10
time (ms)
motor speed (rpm)
(a)
(b)
Figure
6.
Response
of
and
motor
speed
for
PID
controller
without
load
:
(a)
K
p
=
140
;
K
i
=
3
:
3
;
K
d
=
4
:
5
,
(b)
K
p
=
170
;
K
i
=
3
:
3
;
K
d
=
4
:
5
Fuzzy-PID
contr
oller
for
an
ener
gy
ef
ficient
per
sonal
vehicle:
T
wo-wheel...
(Bambang
Sumantri)
Evaluation Warning : The document was created with Spire.PDF for Python.
5308
r
ISSN:
2088-8708
0
1
2
3
4
5
6
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
1
2
3
4
5
6
x 10
4
−20
−15
−10
−5
0
5
10
15
20
time (ms)
motor speed (rpm)
(a)
0
1
2
3
4
5
6
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
1
2
3
4
5
6
x 10
4
−20
−15
−10
−5
0
5
10
15
20
time (ms)
motor speed (rpm)
(b)
0
1
2
3
4
5
6
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
1
2
3
4
5
6
x 10
4
−20
−15
−10
−5
0
5
10
15
20
time (ms)
motor speed (rpm)
(c)
Figure
7.
Response
of
and
motor
speed
for
PID
controller
with
load:
(a)
K
p
=
140
;
K
i
=
3
:
3
;
K
d
=
4
:
5
,
(b)
K
p
=
170
;
K
i
=
3
:
3
;
K
d
=
4
:
5)
,
K
p
=
200
;
K
i
=
3
:
3
;
K
d
=
4
:
5)
By
applying
Fuzzy-PID
that
t
unes
the
PID
parameters,
the
adv
antages
of
ha
ving
appropriate
g
ain
for
certain
condit
ion
can
be
achie
v
ed.
It
is
confirmed
by
the
e
xperimental
results
gi
v
en
in
Figure
8
for
unloaded
and
loaded
e
xperiments.
It
is
seen
from
both
e
xperimental
conditions,
Fuzzy-PID
pro
vides
a
good
performance
by
resulting
smooth
response
on
and
motor
speed.
The
control
parameters,
K
p
and
K
i
,
change
adapti
v
ely
to
reach
the
appropriate
v
alue,
as
seen
in
Figure
9
for
unloaded
and
loaded
e
xperimental
conditions.
The
changing
of
PID
control
parameter
also
contrib
utes
in
reduction
of
po
wer
usage
during
the
op-
eration
of
TWS.
The
Fuzzy-PID
requires
less
po
wer
compared
to
the
con
v
entional
PID
in
both
e
xperimental
conditions,
unloaded
and
loaded,
as
sho
wn
in
Figures
10
and
11.
Hence,
Fuzzy-PID
also
reduces
the
electric
ener
gy
consumed
during
the
operation,
as
sho
wn
in
T
able
2.
In
unloaded
e
xperiment
condition,
Fuzzy-PID
reduces
ener
gy
up
to
157.2%
and
418.28%
compared
to
PID
with
K
p
=
140
and
K
p
=170,
respecti
v
ely
.
Fur
-
thermore,
In
loaded
e
xperiment
condition,
Fuzzy-PID
reduces
ener
gy
up
to
1.31%
and
2.03%
compared
to
PID
with
K
p
=
140
and
K
p
=170,
respecti
v
ely
.
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
−15
−10
−5
0
5
10
time (ms)
motor speed (rpm)
0
1
2
3
4
5
6
x 10
4
−10
−5
0
5
10
time (ms)
θ
(
o
)
0
1
2
3
4
5
6
x 10
4
−20
−15
−10
−5
0
5
10
15
20
time (ms)
motor speed (rpm)
(a)
(b)
Figure
8.
Response
of
and
motor
speed
for
Fuzzy-PID
controller:
(a)
without
load,
(b)
with
load
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
0
50
100
150
200
250
time (ms)
K
p
0
0.5
1
1.5
2
2.5
3
3.5
4
x 10
4
0
0.5
1
1.5
2
2.5
3
3.5
4
time (ms)
K
i
0
1
2
3
4
5
6
x 10
4
0
50
100
150
200
250
time (ms)
K
p
0
1
2
3
4
5
6
x 10
4
0
0.5
1
1.5
2
2.5
3
3.5
4
time (ms)
K
i
(a)
(b)
Figure
9.
K
p
and
K
i
changing
of
Fuzzy-PID
in:
(a)
unloaded
e
xperiment;
(b)
loaded
e
xperiment
Int
J
Elec
&
Comp
Eng,
V
ol.
9,
No.
6,
December
2019
:
5304
–
5311
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
5309
T
able
2.
Comparison
Electrical
ener
gy
consumed
by
the
TWS
during
operation
in
unloaded
and
loaded
e
xperiments
with
con
v
entional
PID
and
Fuzzy-PID.
PID
with
K
p
=140
PID
with
K
p
=170
Fuzzy-PID
unloaded
485.53
Joule
978.51
Joule
188.8
Joule
loaded
3637.2
Joule
3662.74
Joule
3590.02
Joule
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
x 10
4
0
20
40
60
80
100
120
time (ms)
power (watt)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
x 10
4
0
20
40
60
80
100
120
140
160
180
200
time (ms)
power (watt)
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
x 10
4
0
50
100
150
200
250
time (ms)
power (watt)
(a)
(b)
(c)
Figure
10.
Po
wer
consumption
in
unloaded
e
xperiment:
(a)
and
(b)
Con
v
entional
PID
with
K
p
=140
and
170,
respecti
v
ely;
(c)
Fuzzy-PID.
0
1
2
3
4
5
6
7
x 10
4
0
50
100
150
200
250
300
time (ms)
power (watt)
0
1
2
3
4
5
6
7
x 10
4
0
50
100
150
200
250
300
time (ms)
power (watt)
0
1
2
3
4
5
6
7
x 10
4
0
50
100
150
200
250
300
350
400
time (ms)
power (watt)
(a)
(b)
(c)
Figure
11.
Po
wer
consumption
in
loaded
e
xperiment:
(a)
and
(b)
Con
v
entional
PID
with
K
p
=140
and
170,
respecti
v
ely;
(c)
Fuzzy-PID.
5.
CONCLUSION
In
this
paper
,
a
tw
o
wheeled
electric
skateboard
is
designed.
A
Fuzzy
algorithm
is
utilized
for
adapt
ing
the
K
p
and
K
i
of
the
PID
controller
.
In
Fuzzy-PID
method,
the
prior
kno
wledge
of
the
systems
model
is
not
required,
which
is
one
of
its
adv
antages.
The
Fuzzy-PID
method
successes
for
balancing
and
controlling
motions
of
the
TWS.
The
proposed
method
also
reduces
the
electric
ener
gy
consumption
compared
to
the
con
v
entional
PID.
Some
e
xperimental
data
demonstrate
the
performance
of
the
proposed
method.
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5311
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Elec
&
Comp
Eng
ISSN:
2088-8708
r
5311
BIOGRAPHIES
OF
A
UTHORS
Bambang
Sumantri
is
a
lecturer
of
Politeknik
Elektronika
Ne
geri
Surabaya
(PENS),
Indonesia.
He
recei
v
ed
bachelor
de
gree
in
Electrical
Engineering
from
Institut
T
eknologi
Sepuluh
Nopember
(ITS),
Indonesia,
i
n
2002,
M.Sc
(Master
of
Science)
in
Control
E
ngineering
from
Uni
v
ersiti
T
eknologi
P
etronas,
Malaysia,
in
2009,
and
Doctor
of
Engineering
in
Mechanical
Engineering,
T
o
yohashi
Uni
v
ersity
of
T
echnology
,
Japan,
in
2015.
His
research
interest
is
in
rob
ust
control
system,
robotics,
and
embedded
control
system.
Ek
o
Henfri
Binugr
oho
is
a
lecturer
of
Politeknik
Elektronika
Ne
geri
Surabaya
(PENS),
Indone-
sia.
He
recei
v
ed
bachelor
de
gree
in
Electronics
Engineering
from
PENS,
Indonesia,
in
2002,
M.Sc
(Master
of
Science)
in
Intelligent
Mechanical
System,
School
of
Mechanical
Engineering,
Pusan
National
Uni
v
ersity
,
K
orea
in
2009.
His
research
interests
are
in
mechatronics,
robotics,
and
embedded
control
systems.
Ilham
Mandala
Putra
recei
v
ed
bachelor
de
gree
in
Electronics
Engineering
from
PENS,
Indone-
sia,
in
2017.
He
w
as
member
of
PENS
Robotics
T
eam
for
Indonesian
Robotics
Competition.
Rika
Rokhana
recei
v
ed
the
bachelor
and
master
de
grees
in
elect
rical
engineering
from
Institut
T
eknologi
Sepuluh
Nopember
,
Indonesia,
in
1992
and
2004,
respect
i
v
ely
.
She
is
currently
a
Ph.D
student
since
in
Electrical
Engineering
Department,
Institut
T
eknologi
Sepuluh
Nopember
,
Surabaya,
Indonesia.
She
is
a
lso
a
lecturer
of
Politeknik
Elektronika
Ne
geri
Surabaya,
Indonesia.
Her
research
interest
is
in
Medical
Image
Processing
and
Intelligent
System.
Fuzzy-PID
contr
oller
for
an
ener
gy
ef
ficient
per
sonal
vehicle:
T
wo-wheel...
(Bambang
Sumantri)
Evaluation Warning : The document was created with Spire.PDF for Python.