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Experimental implementation of Flower Pollination Algorithm for speed
controller of a BLDC motor
Devendra Potnuru
a,
⇑
, K. Alice Mary
b
, Ch. Sai Babu
c
a
Dept. of Electrical & Electronics Engineering, GVP College of Engineering for Women, Visakhapatnam, AP, India
b
Dept. of Electrical & Electronics Engineering, Gudla Vellore Engineering College, AP, India
c
Dept. of Electrical Engineering, JNTU Kakinada, AP, India
article info
Article history:
Received 14 July 2017
Revised 25 January 2018
Accepted 4 July 2018
Available online 23 April 2019
Keywords:
BLDC motor
DS1103
Flower Pollination
PID tuning
abstract
The design of a good PID controller for speed control of Brushless dc (BLDC) motor is essential for its suc-
cessful operation. This paper presents a design and implementation of recently developed nature inspired
Flower Pollination Algorithm for speed control of BLDC motor with optimal PID tuning. In the present
work, optimization based approach is applied for tuning of PID speed controller by considering an inte-
gral square error as the objective function. The effectiveness of the presented work is compared with the
conventional Ziegler-Nichols method and other existing nature inspired optimization methods such as
PSO and Firefly algorithms, and the same is validated through real-time implementation with dSPACE
DS1103 controller board.
Ó2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-
nd/4.0/).
1. Introduction
Electrical motor is an essential element for most of the real-life
applications. Widespread use of electric motor drives of various
sizes is operational throughout the world, and they have significant
impact on the energy saving when energy efficient electric drives
[1] are being developed. In the recent past, the conditions have
been changed in adjustable speed drives due to the availability of
power semiconductor devices with ratings up to 6000 V and
3000 A without even connecting a series or paralleling the devices
[2–4]. The three phase BLDC motors have been rapidly emerging in
many industrial, household, commercial and automotive applica-
tions over the past several years because of its advantages like con-
trol flexibility, high torque capability, noiseless operation, more
efficiency, lesser size and volume as compared to the conventional
motors. Hence it is reducing fuel emissions and consumption.
There are basically two types of Brushless DC (BLDC) motors,
viz. Permanent Magnet Synchronous Motor (PMSM) and BLDC
motors depending on their flux distribution. The motor which
has a trapezoidal wave shape is called as a BLDC motor, whereas
the PMSM has a sinusoidal back-EMF wave shape. The control of
BLDC motor can be classified as sensor-based control and sensor-
less control. In sensor-based control, the stator winding is excited
based on rotor position which is measured using hall sensors [5].
PID controllers are commonly used in speed control of BLDC
motor. However, the performance of a speed controller mainly
depends on tuning of PID gains. Tuning is nothing but finding
appropriate proportional, integral, and derivative gains of PID con-
troller to meet the desired performance. Tuning of the PID con-
troller is a complex task which is mainly done by either trial and
error or rule based methods. The Ziegler-Nichols tuning method
is the most well-known tuning method based on thumb rules.
However, the manual tuning approach will take more time and
may damage the hardware involved in the process of control. Fur-
ther, rule based approaches sometimes may not support to certain
higher order plants and systems with no time delay or little time
delay. Recently, researchers have proposed several optimization
based approaches for many applications by selecting an integral
square error (ISE) as the objective function for PID tuning. A new
metaheuristic optimization Bat algorithm for Power System Stabi-
lizer based on PID controller is designed in [6]. A Bacterial Foraging
Optimization (BFO) technique is employed for PI controller of Per-
manent Magnet Synchronous Generator (PMSG) to extract the
maximum power from the wind in [7]. A classical Hill Climb
Searching technique using ANN is applied for Switched reluctance
https://doi.org/10.1016/j.asej.2018.07.005
2090-4479/Ó2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑
Corresponding author.
E-mail addresses: devendra.p@gvpcew.ac.in,devendra.p.07@gmail.com
(D. Potnuru).
Peer review under responsibility of Ain Shams University.
Production and hosting by Elsevier
Ain Shams Engineering Journal 10 (2019) 287–295
Contents lists available at ScienceDirect
Ain Shams Engineering Journal
journal homepage: www.sciencedirect.com
generator in [8]. Genetic Algorithm Method is being applied for
tuning of PID controller in different applications in [9,10]. Particle
Swarm Optimization (PSO) is applied in [11,12]. Self-tuning of
fuzzy PID controller with model reference adaptive control (MRAC)
for a BLDC motor is developed in [13]. Moreover, PSO and Bacterial
foraging technique has been designed for BLDC motor in [14].
Nature inspired algorithms are developed to solve many engi-
neering problems, viz. Genetic Algorithm is based on the evolution
of biological systems, swarm behaviour of birds, fishes are used in
Particle Swarm Optimization (PSO) method, bat-inspired algorithm
is imitating the behaviour of micro bats and similarly flashing light
patterns of fireflies in the fire-fly algorithm.
However, all the conventional optimization methods do not
solve the complex engineering problems associated with higher
nonlinearity. The Flower Pollination algorithm is an efficient
nature-inspired metaheuristic algorithm can circumvent the chal-
lenges associated with higher nonlinearity of the problem. In the
present work, PID gains are tuned for the non-linear BLDC motor
using Flower Pollination Algorithm which utilizes the pollination
behavior of flowered plants. The efficacy of the work has been val-
idated through real-time implementation with dSPACE DS1103
controller board. In addition one can implement recently proposed
methods in [15–17] for sensorless speed control of BLDC motor
with the Flower Pollination Algorithm.
2. Dynamic equations of BLDC motor
The dynamic equations of the 3-phase BLDC motor are given in
(1)
d
dt
i
1
i
2
i
3
x
h
2
6
6
6
6
6
6
4
3
7
7
7
7
7
7
5
¼
R
L
L
s
i
1
e
ab
L
s
þ
1
L
s
V
ab
R
L
L
s
i
2
e
bc
L
s
þ
1
L
s
V
bc
R
L
L
s
i
3
e
ca
L
s
þ
1
L
s
V
ca
1
J
ðT
e
T
L
Þ
B
J
x
P
2
x
2
6
6
6
6
6
6
6
6
4
3
7
7
7
7
7
7
7
7
5
ð1Þ
T
e
¼e
a
i
a
þe
b
i
b
þe
c
i
c
x
ð2Þ
When they are formulated in state space representation and
looks as in (3)
d
dt
i1
i2
i3
x
h
2
6
6
6
6
6
4
3
7
7
7
7
7
5
¼
R
L
L
s
00k
e
L
s
xab 0
0R
L
L
s
0k
e
L
s
xbc 0
00R
L
L
s
k
e
L
s
xca 0
k
e
Jxab k
e
Jxbc k
e
Jxca B
J0
000 P
20
2
6
6
6
6
6
6
6
6
4
3
7
7
7
7
7
7
7
7
5
i1
i2
i3
x
h
2
6
6
6
6
6
6
4
3
7
7
7
7
7
7
5
þ
1
L
s
000
01
L
s
00
001
L
s
0
0001
J
0000
2
6
6
6
6
6
6
6
4
3
7
7
7
7
7
7
7
5
Vab
Vbc
Vca
TL
0
2
6
6
6
6
6
6
4
3
7
7
7
7
7
7
5
ð3Þ
where L
s
¼LM,i
1
¼i
a
i
b
;i
2
¼i
b
i
c
;i
3
¼i
c
i
a
and i
a
,i
b
and i
c
are three phase stator currents.
Similarly i
1
,i
2
and i
3
are line currents in star connected stator
winding. Where x
ab
;x
bc
;x
ca
are the functions of back-EMFs depend-
ing on the rotor position of BLDC motor and L, self –inductance, Mis
the mutual inductance of stator winding. R
L
¼Stator resistance
(line-line),
x
m
=rotor angular speed in rad/sec, J = rotor inertia,
T
e
= torque developed in the motor, T
L
external load torque and
P = the number of poles [18] and motor parameters are as shown
in Table 1.
2.1. BLDC motor drive scheme
The closed loop speed control of BLDC motor drive scheme is
shown in Fig. 1. The error between command and measured speeds
is fed to the PID controller. The PID controller generates torque
command for BLDC motor. Then the reference torque is scaled with
the motor torque constant to obtain the magnitude of reference
current. Further, reference current in each phase depends on the
rotor angular position [18] and are fed to hysteresis current con-
troller. The hysteresis current controller generates control signals
to turn on the inverter switches based on the current error [19–21].
The efficacy of speed control mainly depends on the Speed con-
troller PID gains. In the present work the PID gains are obtained
through the Flower Pollination Algorithm by considering the inte-
gral square error as objective function. The global best values of the
PID gains are utilized. The block diagram for PID tuning for closed
loop speed control of the BLDC motor drive using Flower Pollina-
tion Algorithm is shown in Fig. 2. For more details of the drive
scheme one can refer [22].
3. Flower Pollination algorithm
In the year 2012, Xin-She Yang proposed this algorithm which
emulates the natural behavior of Pollination process involved in
Table 1
BLDC Motor parameters.
Resistance (Line-Line) R
L
¼1:5
X
ðline lineÞ;
Number of poles P = 6
Power 1.5hp
Moment of inertia J = 8.2614 e-5 kg.m
2
Inductance L-M = 6.1mH (line-line)
Voltage V = 310 V
Torque constant (Nm/A) Kt = 0.2148
Fig. 1. A typical BLDC motor drive scheme.
288 D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295
the flowering plants. Insects like butterflies, birds, animals bats etc.
are the pollinators in the process of pollination. There are two forms
of pollination methods viz. biotic and abiotic. Method in which pol-
linators like insects or birds are involved in pollination is called bio-
tic and in contrary the pollination without any external pollinators
is called as abiotic. About 90% of pollination occurs through biotic
pollination. Further, pollination process can also be classified as
self-pollination or cross-pollination [23]. The cross-pollination is
nothing but transfer of the pollen from one flowering plant to
flower of a different plant whereas the self-pollination is related
to pollination in the same flowering plant from one flower to other
flower where it does not require any pollinator. The pollination
between flowers at longer distances is called as global pollination.
Hence in the algorithm, these pollinators may jump long steps
and must follow the L
´evy distribution function. Furthermore,
flower constancy can be used an incremental step based on the sim-
ilarity or difference of two flowers. One can read more details of the
algorithm in [23–25]. The major characteristics of the flower polli-
nation algorithm are described from (4)–(7).
x
tþ1
i
¼x
t
i
þLðx
t
i
g
Þð4Þ
x
tþ1
i
¼x
t
i
þ
e
ðx
t
i
x
t
k
Þð5Þ
where x
t
i
the actual desired result vector, g
is the current best of the
optimization problem, eis a random number with uniform distribu-
tion and L is a step size obtained from a Lévy distribution. Lévy
steps of involved in the global pollination are shown in (6)
L¼1
s
1þb
ð6Þ
where bis called the exponent of Lévy function.
Generalizing the Lévy steps is a challenging task; however s is
calculated from (7).
s¼u
j
v
j
1þb
withuN0;
r
2
anduN0;1ðÞwhere
r
is a function of
ð7Þ
In the present work, the Flower Pollination Algorithm is applied
for closed loop speed control of BLDC motor by considering the
integral square error as objective function as given in (8) which
is nothing but minimization of (P
N
k
k¼1
j½
x
m
ðkÞ
x
m
ðkÞ
2
j) with time.
ISE ¼min Z
s
0
eðtÞ
2
dt
ð8Þ
Flow chart for implementation of the algorithm is shown in
Fig. 3 and their settings are: Population: 50, Absorption coefficient
c
: 1, Mutation coefficient
a
: 0.2, Attraction coefficient b: 2, Damp-
ing ratio: 0.98 and Iterations: 500.
4. Experimental implementation
Hardware implementation of the proposed work is shown in
Fig. 4. In the present work, a BLDC motor with incremental encoder
is used to measure angular position and speed of the motor. The
Fig. 3. Flower Pollination Algorithm implementation [24].
Fig. 2. The BLDC motor drive scheme with PID tuning in closed loop control.
D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295 289
dSPACE DS1103 controller board has been facilitated with A/D and
D/A converters. The board is equipped with a slave DSP processor,
TMS320F240 for enabling advanced I/O functions [26]. The connec-
tor panel CLP 1103 of the dSPACE controller board is used to con-
nect the encoders, hall position sensors and current transducers
etc. [24–26]. In the proposed work, incremental encoder with a
pulse count of 2000 pulses/revolution is used for measuring speed
and position [26,28]. The communication with the computer is
performed through an optical fiber cable.
The Intelligent Inverter used as Voltage Source Inverter which
consists of various subsystems such as Rectifier & Filter, Inverter
circuit, Optoisolator, Gate Driver, Current Sensor, Signal condi-
tioner and Protection Circuit. Interfacing of encoder is done by
RTI blocks facilitated in the DS1103 controller board. The devel-
oped MATLAB/Simulink simulation file with embedded functions
and high level inbuilt functions are connected to RTI (Real Time
Interface) blocks of dSPACE DS1103. The RTI blocks facilitate the
reading of encoder values, Analog inputs and digital inputs to the
MATLAB/Simulink environment. The dSPACE DS1103 also consists
of real-time software called Control Desk which is used for online
variation of parameters and data acquisition [27]. For more details
of real-time implementation issues and rapid control prototyping
one can find in [28]. The snapshot of laboratory implementation
of the presented work is illustrated in Fig. 5.
5. Result analysis and discussion
Performance of the proposed algorithm has been investigated in
hardware implementation with a laboratory setup as shown in
Fig. 5. The performance of closed loop speed control is tested for
different scenarios such as ramp, stepped, sinusoidal, and step
speed commands.
The effectiveness of the proposed method for closed loop con-
trol of the BLDC motor drive is compared with conventional
Zeigler-Nichols PID tuning, and also with some well-known nat-
ured inspired optimization algorithms such as Firefly and PSO in
terms of absolute mean error. The absolute mean error is calcu-
lated for definite number of samples using the Eq. (9).
Absolute mean error ¼1
N
k
X
N
k
k¼1
j½
x
m
ðkÞ
x
m
ðkÞj ð9Þ
Fig. 5. Snapshot of experiment test bed for the BLDC motor drive top view.
Fig. 4. Hardware implementation scheme for BLDC motor drive.
290 D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295
where x
m
reference (command) is speed and x
m
is actual speed of
the motor.
5.1. Scenario-1: speed tracking performance for ramp type reference
speed
In this case, a ramp type reference speed is considered for the
closed loop speed control. The Fig. 6 shows the speed control per-
formance of the drive by Flower Pollination based PID controller in
comparison with Zeigler-Nichols, Firefly and PSO algorithms for
the ramp type speed command. The transient as well as steady
state performance using the flower pollination is better than other
methods.
5.2. Scenario-2: speed tracking performance for a step reference speed
To determine the transient and steady state performance of the
closed loop speed control of BLDC motor, experiments are
Fig. 6. Speed tracking performance of the drive for ramp speed command.
Fig. 7. Tacking performance comparison of Flower Pollination for Step command speed.
D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295 291
Fig. 9. Tracking performance comparison of Flower Pollination for staircase reference speed.
Fig. 8. Tracking performance comparison of Flower Pollination for Sinusoidal command speed.
292 D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295
performed on the step speed command of 300 rpm. From the Fig. 7
one can observe that the closed loop speed control performance of
the drive is superior in transient as well as steady state conditions
with Flower pollination.
5.3. Scenario-3: speed tracking performance for a sinusoidal reference
speed
A sinusoidal command speed is considered for this case. The
closed loop speed control performance is demonstrated in Fig. 8
Table 2
Performance indices for closed loop speed control.
Algorithm Maximum error Absolute Mean error Standard deviation
Ramp Speed command
Flower Pollination 24.38739 2.911762 2.911661
Firefly 31.730921 3.219044 3.818440
PSO 22.582270 3.038850 3.063571
ZN 45.501490 5.470105 6.668917
Staircase Speed command
Flower Pollination 21.596598 2.489160 2.383329
Firefly 26.015237 2.707781 2.563383
PSO 24.571957 2.716240 2.632258
ZN 62.265064 5.424426 5.645016
Step Speed Command
Flower Pollination 98.234736 2.867569 5.262460
Firefly 109.718844 3.092901 6.302494
PSO 107.390476 2.948584 5.465794
ZN 107.660812 4.801828 7.265611
Sinusoidal Speed Command
Flower Pollination 200.638146 4.659365 16.287181
Firefly 296.537299 5.724696 21.155912
PSO 302.931754 6.061821 21.554355
ZN 297.325763 6.403736 22.504244
Fig. 10. Control parameters (Step Command):Toque (Nm), Line Voltage (volts), Rotor Angular Position (Radians), Phase Current (A).
D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295 293
and one can witness that the performance with Flower Pollination
algorithm is better as compared to other algorithms even for sinu-
soidal reference speed.
5.4. Scenario-4: speed tracking performance for a stepped reference
speed
In this case, a stepped reference speed is considered and the
dynamic performance for a stepped speed command using the
Flower pollination is as shown in the Fig. 9. One can observe that
the motor tracks the reference speed so closely using the Flower
Pollination.
The performance comparison of closed loop PID control for
BLDC motor has been represented in Table 2 in terms of absolute
mean error, maximum speed error and standard deviation for dif-
ferent reference speeds. The plots of control parameters such as
torque (Nm), line voltage (volts), rotor angular position (Radians),
phase current (A) are shown in Fig. 10 for step reference speed.
The absolute speed mean error of 2.911762 rpm with Flower Polli-
nation algorithm for ramp reference speed, similarly 4.659365 rpm
for step speed command, 2.867569 rpm for sinusoidal speed refer-
ence and 2.489160 rpm for staircase reference speed. Further, one
can observe that the superior performance of the Flower Pollina-
tion algorithm with the absolute speed error plots is shown in
Fig. 11. The parameters considered in the optimization algorithms
are given in Table 3 and the PID gains considered for the experi-
mentation are shown in Table 4.
6. Conclusions
Flower Pollination algorithm has been implemented for closed
loop speed control of the BLDC motor. It is observed that absolute
mean speed error in closed loop speed control with Flower Pollina-
tion Algorithm is negligibly small as compared to PSO, firefly and
Zeigler–Nichols method. The drive performance is also demon-
strated for different types of reference speeds. However, this
approach is suitable for testing the performance of the drive in
off line with fixed PID gains but has given superior performance
characteristics as compared to conventional methods. All the
results are experimentally validated for BLDC motor drive using
dSPACE DS 1103.
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Table 3
Parameters considered in the optimization algorithms.
PSO Firefly Flower Pollination
Population: 50 Population: 50 Population: 50
C1 = 0.12
C2 = 1.2
weight factor: 0.9
Absorption coefficientc=1
Mutation coefficienta= 0.2
Attraction coefficient b:2
Damping ratio:0.98
Switch
probability p = 0.8
Fitness function: ISE Fitness function: ISE Fitness function: ISE
Iterations: 500 Iterations: 500 Iterations: 500
Table 4
Final global best values of PID gains for closed loop control of BLDC motor drive.
PID tuning method Best values of PID Parameters obtained
k
p
k
i
k
d
ZN 0.5 1.5 0.09
PSO 26.4960 12.4716 0.3288
Firefly Algorithm 8.9552 2.63 0.0621
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Dr. P. Devendra is currently working in GVP college of
Engineering. He has completed M.E from Anna Univer-
sity; Chennai. Ph.D from JNTU Kakinada, Andhra Pra-
desh. He has around 16 years of teaching experience.
His areas of current interests are sensorless speed con-
trol, Energy conservation, Energy efficient drives and
Adaptive control systems.
Dr. K. Alice Mary received B.E from Govt.BDT CE&T,
Davanagere, Karnataka, India in 1981, M.E in the year
1989 from IIT-Roorkee and PhD from IIT- Kharagpur in
the year 1998. She is in teaching profession from
1981onwards and now working as Professor in Gud-
lavalleru Engineering Colege, Gudlavalleru, AP..To her
credit, she received many prestigious awards for her
achievements in academic performance at national
level. Her research interest are control systems appli-
cations to power electronics and machinedrives
Dr. Ch. SaiBabu received his B. Tech degree in Electrical
Engineering from Andhra University Vishakhapatnam,
A.P, and his M. Tech degree in Machines and Industrial
drives from REC Warangal, A.P. and his Ph.d degree in
Electrical Engineering from JNT University Hyderabad,
A.P in 2004. He is currently working as Professor in dept.
of Electrical Engineering. To his credit, he received many
prestigious awards for his achievements in academic
performance at national level. His main research inter-
ests include energy efficient drives, nonlinear control of
Machines, Nonconventional energy and Power quality.
D. Potnuru et al. / Ain Shams Engineering Journal 10 (2019) 287–295 295
