Speed Control of a BLDC Motor using fuzzy logic controller

1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016)
Speed Control of a BLDC Motor
using Fuzzy Logic Controller
Adil Usman l and Bharat Singh Rajpurohit2
'Student Member, IEEE, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi
2Senior Member, IEEE, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi
E-mail: [email protected]
Abstract-The speed control of the Permanent Magnet
Brush Less Direct Current (PMBLDC) Motor is of high
importance since it indirectly controls the mechanical output
required and hence the efficiency. The control on this
parameter has been demonstrated in various papers using
various controllers like PWM, PI, Fuzzy and Neural
Networks (NN) etc. In this paper an attempt has been made
to analyze the result of PI Controller, by using the
appropriate Kp and K j value in order to get the constant
control on the speed of a Brush Less Direct Current (BLDC)
motor. Further an attempt as an assessment has been made
in designing an alternative controller to minimize steady
state error and obtain better result for the control of the
speed parameter of a BLDC Motor. A Fuzzy Logic
Controller (FLC) has been designed to compare the PI
Controller output with the FL Coutput. The results are then
compared and analyzed. It is thereby concluded that the
FLC offers better adaptability than the normal linear PI
controller and that the PMBLDC drive offers better steadystate and dynamic performances.
Keyword-Fuzzy Logic Controller (FLC); Permanent
Magnet Brush Less Direct Current (PMBLDC); Permanent
Magnet Synchronous Motor (PMSM)
EMF for dynamic simulation . Cahin, Murat et al. [3] in
their paper emphasizes on the new technique of
co-simulation study of BLDC Drive. Labview and
Multisim programs are used as a platform performs cosimulation studies on the BLDC model. Samitha Ransara,
H. K. , and Udaya K. Madawala [4] presents a new buck
converter based modeling technique for Brushless DC
(BLDC) motor drives which has reduced computational
complexity, better performance and low cost. While Zhao
Long, et.al [5] presents a strategy for the control of torque
using current observer and state feedback control
algorithm for a BLDC motor.
The desired level of performance from BLDC motor
could be achieved by the use of suitable speed controllers
in the overall electric drive-system. Many controllers like
PI, FLC and NNare available for the speed control of such
electric drives [6]. The Proportional plus Integral (PI)
controller; is the most commonly used standard controller
applicable for speed control of electrical drives. Due to the
simple control structure and ease of implementation; PI
controllers are widely used in the industrial sector. These
controllers at the same time pose some difficulties such as
control complexity nonlinearity, load disturbances and
parametric variations [7].
The use of faster dynamic response controller in motion
control like Artificial Intelligence (AI), Adaptive NeuroFuzzy Inference Systems (ANFIS); is the substitution of a
standard (PI) controller. FLC speed controller is one the
frequently accessed controller used for the speed control of
an electric drive. Fuzzy logic speed control can sometime be
seen as the ultimate solution for high-performance electrical
drives [8]. PI controller when compared with these recent
emerging controllers, found to be comparatively inefficient.
The reason for low efficiency in the PI controller is the high
overshoot from the reference point, which leads to transients
and large delay time to get into steady state. The slow
response on the sudden change of load torque and the
sensitivity to controller gains (Kpand K;) are the other reasons
for the obsoleteness of PI controllers [9]. This has resulted in
the increased demand of modern nonlinear control structures
like Fuzzy logic controller. These controllers are inherently
robust to load disturbances. BLDC motors being non-linear
in nature can easily be affected by the parameter variations
and load disturbances [10].
I. INTROD UCTION
Nowadays the use ofBLDC motor instead of brushed
DC motor has increased in number of power electric drive
applications. BLDC motor comprise of sinusoidal
(PMSM) or trapezoidal (PM BLDC) motor, depending
upon the rotational voltage (back EMF) induced. Due to
the fact of recent advancements in technology, these
motors which are categorized as special electrical motors
are much more suitable for efficient drive operation. These
motors are characterized by a much higher efficiency,
greater reliability, and more power density requiring less
maintenance. Due to the fact that PM BLDC has higher
torque delivered to motor size ratio, high efficiency and
long life; these motors find their application in various
electrical systems depending upon the requirements. In
this context it can also be noticed that from last few years,
research in this area have experienced an expansion.
Balogh et. al [1] and Hong Wonbok, et.al [2] in their
paper proposes the modeling of a BLDC motor along with
the simulation analysis in the MA TLAB tool. BLDC
motor drives model is developed considering the behavior
of a motor during commutation and waveform of back978-1 -4673-8587-9/16/$31 .00 ©2016 IEEE
[1]
1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016)
Papers [11]-[12] discussing detail about the working
and construction of PMBLDC with varying alignment of
winding configurations trapezoidal and sinusoidal back
EMF. The voltage and torque equations can be derived from
these papers further which are discussed in this paper too.
Where paper [13]laid more stress on Hall Effect Sensors for
a BLDC motor analysis, paper [14] describes about the
speed control ofBLDC using FLC control algorithm.
N. Parhizkar et. al [15] in his paper describe in detail
about the torque control strategy for a given BLDC motor
explaining the use ofFLC for different logical approaches.
Paper [16] compares the performance of a BLDC motor
by a comparative study of PI and FLC. Certain approach
has been found common as can be seen in [17] where
some simulations have been performed and demonstrated
using both PI and FLC but with certain drawbacks which
are overcome in this paper.Some algorithms are advanced
and offer better steady-state and dynamic performances
which are illustrated too in this paper.
The speed controller provided is generally a PWM
controller as shown in Fig. I. In context to this, the BLDC
motor is considered as an equivalent to an inverted DC
commutator motor in which the conductors remain
stationary while the magnets rotates [13]. Fig. I. shows the
basic BLDC motor model of electrical drives with a PWM
control system based on which the following equations are
derived.
Modeling of a BLDC is similar to that of synchronous
machines. The motor is fed by a three-phase voltage
source inverter. Square or sinusoidal wave shape can be
applied as the input voltage since the peak voltage does
not exceed the maximum limit of the motor. Similarly, the
model of the armature winding for the BLDC motor is
expressed as:
The BLDC motor also referred as an electronically
commutated motor have no brushes on the rotor and the
commutation is performed electronically at certain rotor
positions [12]. The windings of the stator phase can be
configured in multiple ways. It could either be inserted in
the slots of the stator or can be wounded as one coil on the
magnetic pole. Since it is known that the back EMF of a
BLDC motor is Trapezoidal in nature, therefore the
magnetization of the permanent magnets and their
displacement on the rotor are chosen accordingly.
Henceforth a rectangular shape, three-phase voltage
system is developed to create a rotational field with low
torque ripples. Also the trapezoidal back EMF with square
wave currents, generate the constant torque. As it is
generally observed that in a conventional BLDC motor
drive the motor is driven via a six-switch three phase
inverter which is connected to the stator windings of the
motor. The commutation is provided by three Hall-effect
position sensors which provide six commutation points for
each electrical cycle.
I~
Co;:;-tr.;! Sy-;te;;:;
(E)""':
I
,--,+_ _
"0_"..8
N:
Halle
S
:
(5)
W
There are several controllers available nowadays like
proportional integral (PI), proportional integral derivative
(PID) Fuzzy Logic Controller (FLC) or the combination
between them: Fuzzy-Neural Networks, Fuzzy Genetic
Algorithm, Fuzzy-Ants Colony, Fuzzy-Swarm. But as
within the scope of this paper the discussion on the PI and
Fuzzy Logic Controller will be discussed as below.
PI Speed Controller
A Proportional Integral (PI) is a feedback control loop
mechanism used in electrical control system. PI Controller
finds its applications in many industrial processes where a
controller attempts to correct the error between a
measured process variable and reference set point. The
algorithm involves a calculation and outputting of a
corrective action which is done in order to adjust the
process accordingly. The PI controller, as the name
indicates, involves two separate modes that are: the
proportional mode and integral mode. The proportional
JI~
I I ~~~~
-
(3)
III. BLDC SPEED CONTROLL ERS
A.
S
1_ .
~=~~F~+~
ii
~
-
(2)
where,
W m is Angular speed of rotor.
8 m is Mechanical angle of rotor.
8 e is Electrical angle of rotor.
F(8 e) is Back-EMF reference function of rotor
position.
.wM,cr:~
(Gt -flt----'t-----oI I
Vb
2n:
BLOC Motor
". T
(1)
dEb) + Eb
= Rblb + L (dt
Vc=Rele+L(:;)+Ee
where,
Va' Vb' Vcare the phase voltages
la' lb' Ie are the phase current
Ea, Eb, Eeare the back EMFs
The back EMFs can be expressed as,
Ea = KewmF(8e) (4)
2n:
Eb = Ke wmF(8 e -"3)
II. DYNAMIC MODEL AND STRUCTURE
OF A PERMANENT MAGNET BLDC MOTOR
3 Phase Voltage
Sourc e Invert e r
Va=Rala+L(:;)+Ea
CUm
N
'
M echa n ical System
Fig. I: BLDC Motor Model of Electrical Circuits [13]
[2]
1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016)
mode determines the reaction to the current error whereas
the integral mode determines the reaction based recent
error [6]. Due to its simple structure and ease of use; PI
controller is widely used in industry.
Fig. 2. describes the PI Controller based PMBLDC
drive. The drive consists of speed controller, reference
current generator, PWM current controller, position
sensor, the motor and IGBT based current controlledvoltage source inverter (CC-VSI).
of plant which is generally complex mathematical
equations. On the other hand, FLC expresses operational
laws in terms oflinguistics terms instead of mathematical
equations [ll].Sometimes it has been experienced that
there are many systems which are too complex to model
accurately, even with complex mathematical equations;
therefore conventional methods become infeasible in these
systems. Henceforth, fuzzy logics linguistic terms provide
a feasible and easy method for defining the operational
characteristics of such system to design and implement
[15]. The generalized block diagram of a BLDC Motor
with a controller as shown in Fig. 2, can be replaced by
any other controller as required. Any control mechanism
can be adopted in the system such as PI, PID or Fuzzy
Logic Controller which will be suitable to maintain and
control the speed of the BLDC Motor.
The section ahead explains the Simulink model of a
BLDC Motor with a PI and a Fuzzy Logic Controller
along with the simulations results which is discussed in
section IV.
Rotor Position Feedback
Fig. 2: Block Diagram ofa BLDC Motor with Controller Scheme
IV.
The speed of the motor is compared with its reference
value and the speed error is processed in proportionalintegral (PI) speed controller. The flowchart in Fig. 3
shows the speed control algorithm of a BLDC Motor. The
speed loop of the typical BLDC motor is generalized in
this flowchart and is common for any type of controllers
used [16].
B.
SIMULATION AND RESULTS
Simulink model of a Permanent magnet BLDC Motor
with the PI controller is designed in a Matlab Simulink
tool. The Simulink model consists of a 3phase supply via
inverter and a BLDC motor. The model is coupled with a
PI Controller for the speed control of the motor.The above
model has been designed using the following parameters
as shown in Table I.
Fuzzy Logic based Speed Controller
TABLE I : P ARAMETERS CHART
Start
Parameters
Speed (N in RPM)
Voltage (Vin volts)
Poles of the Motor (P)
Motor phases (<p)
Stator Phase Resistance (Rs in ohm)
Value
1500
160
4
3
0.7
Torque Constant (k)
Load Torque
Back EMF area (degree)
0.84
2 N-m
120
Rotor Initial Position (8 in degrees )
Kp=Proportional Constant
Ki=Integral Constant
YES
0
0.002
5
The Simulink model is been a given a Run Time of
0.5 seconds and the system is simulated for different
values of the parameters as shown above.
Acceleration
A.
PI Controller
The Simulink model of the BLDC Motor with a PI
Controller has been shown in Fig. 3. The model is a
MA TLAB Simulink model with the PI Controller;
controlling the speed of the motor. The model consists of a
voltage source inverter, a BLDC Motor and the speed of
1500 rpm is set as the reference value. The BLDC motor
follows the same operation principle as discussed in
section II.
Fig. 3a: Basic Flow Chart ofBL DC Motor Speed Control
Non-Linear Systems can be very easily modeled by
Fuzzy Logic Controller (FLC). The conventional control
system design is usually based on the mathematical model
[3]
1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016)
The following results were observed on running the
PI Controller based PM BLDC motor.
Fig.3.(b), (c) and (d) shows the speed torque and
current output of the BLDC controlled by PI controlled
mechanism. It can be observed that the speed of 1500 rpm
is maintained after certain delay after which the speed
remains constant throughout. The torque obtained is in the
range of l.5 to l.6 Nm. The Controller thus designed with
the specific constant value of Kp and Ki gives the desired
result which is verified.
/1A ex) = max(min(x-a, ~~\ 0)(7)wherea,b,c are
b-a c-b
scalar parameters
TABLE 2 : ERRORS AND CHANGE IN ERROR AS INPUT TO THE FLC
Error (e)
1500-1507
1507-675
675- 150
250-150
150-1 S
IS-1.5
1.5-0
~~
~ ' ••••~
I .•• • • • ~
l : • •~
: •.•• .•.•~
j •••• •••• • •
o
~
~
"
u
Time (sec)
u
g
g
20 ,r-----.------.------.-----~----~
_ 15
E
,;
[10
>-
5U
~~\Ulli\j
Ol~--~~----~----~~----~----~
o
0.05
0.1
0.15
0.2
0.25
Time (sec)
Fig. 3c: Motor Torque Obtained using PI Controller
~~
~
•
~
"
Time (sec)
u
g
U
Fig. 3d: Stator Current with PI Controller
B.
Fuzzy Logic Controller
The Simulink model consists of the same components
as above; a 3-phase supply via inverter and a BLDC
motor.The FLC has been designed using the input and
output parameters of the PI controller; which has Member
functions as 7 and type of functions used is triangular. The
formation ofFLC involves:C.
Output
0
0-76
76-127
97-127
127-135.7
135.7-136.7
136.7-136.9
The inputs to the FLC are error (e) and change in
error (ce) while the output obtained is active component of
current. Table II shows the inputs and outputs of the FLC.
Both ' e' and ' ce' are converter from continuous data to
fuzzy data by using seven membership functions. The
membership functions used is triangular in shape due to its
advantage of having simplicity in construction. The seven
membership functions are named as ' NB' -Negative Big,
'NM' -Negative Medium, 'NS ' -Negative Small, ' ZE' Zero, 'PS' -Positive Small, 'PM'-Positive Medium, ' PB' Positive Big. The discrimination of membership functions
are done on the basis of different range of values for ' e'
and ' ce' . The output of fuzzy logic controller is calculated
using Table III. For seven membership function of each
input, the output will be 49. For suitable values of inputs,
an output is found from Table III. Since FLC is designed
by expert knowledge and does not involve any
mathematical modeling of the system, therefore both the
above numerical input and output variables are converted
to linguistic variable by a process called Fuzzification
using seven fuzzy sets.
Fuzzy sets of error and change in error are combined
to produce rules for the system. These rules are known as
rule base of the system which is shown in Table III.
Through the knowledge of fuzzy; an inference is drawn
which gives the output of the system.
Based on the above look up table, a BLDC model
using FLC has been developed in a MATLAB Simulink
tool and the simulation is carried out.
Fig.3b: Rotor Speed Observed to be Constant using PI
.'l
Change in Error (ee)
1500-1507
1507-675
675-150
250-150
150-1S
IS-1.5
1.5-0
TABLE 3: RULE BASE MA TRIX FOR OUTPUT
e/ce
NB
NM
NS
Formation of Fuzzy Logic Controller
1.
2.
Seven fuzzy sets for both inputs and outputs
Fuzzification of inputs using continuous universe
of discourse
3. Connotation using Mamdani' s 'min ' operator
4. Defuzzification using ' centroid' method
Triangular membership function is based on equation
of straight line which for the ease of computation is used
to design fuzzy sets. This triangular curve can be
represented as
ZE
PS
PM
PB
NB
NB
NB
NB
NB
NM
NS
NM
NB
NB
NB
NM
NS
NS
NB
NB
BM
NS
ZE
ZE
PS
PS
PM
ZE
ZE
NB
NM
NS
ZE
PS
PM
PB
PS
NM
NS
PM
NS
ZE
PS
PM
PB
PB
PB
PS
PM
PB
PB
ZE
PB
ZE
PS
PM
PB
PB
PB
PB
Fig. 4 shows the PM BLDC Simulink model with a
FLC and the simulation results for the same have been
shown ahead. The simulations results comprise of speed,
torque and current characteristic curve of a BLDC motor
with FLC. These results are further compared with the
results obtained with PI controller.
[4]
1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016)
V. CONC LUS IONS
The speed control of a Permanent Magnet BLDC
Motor is presented in this paper, using both PI controller,
and Fuzzy Logic Controller. The paper explains about the
performance analysis of a BLDC Motor in brief Further a
comparative study has been discussed between the PI
controller and Fuzzy Logic controller used on the
MA TLAB Simulink tool for the speed control of a BLDC
motor. The results obtained using both the controllers
separately are then compared and analyzed to evaluate the
speed controllers' performance. The inference which can
be concluded after comparison is that speed control of
BLDC using Fuzzy Logic Controller has better
performance. To add current control function to the
proposed speed controller in order to keep the current
within a certain range for a specific speed, could be a
work for future. The proposed future work would thereby
enhance the motor start-up current, reduce the motor
current ripples and overall enhance the motor torque
characteristics performance. Current control methodology
will also reduce the speed and torque variations caused
due to any sudden changes in the motor current value .
I-,S.,
o
Fig. 4a: Simulation Mod el of the Speed Control of BLOC using FLC
~t:. :. .:..t... :. :. : . I. : . :. :. t. :.:.: . :. t..: . : .: . I. . : . .:: . t:. . . ::..j
3000
o
Il
o
.
oos
01
015
T_I'tc)
02
025
03
035
Fig. 4b: Rotor Speed Observed to be Constant with FLC
I
DOS
01
I
01!i
I
02
I
025
I
r ..... (.tcl
I
03
035
I
04
I
O"!i
I
Fig. 4c: Motor Torque for a BLOC Motor with FLC
2 ............. : .......................
j........................ ~ ....................... :........................ f ......................
~~
-4
I
05
005
01
015
02
025
ACKNOWLEDGEMENT
03
The author would like to thanks the Department of
Electronics & Information Technology (DeitY)and Naval
Research Board,for financial support.
Timl(.K)
Fig. 4d: Stator Current in a BLOC motor using FLC
In Section IV two controllers: PI and FLC are used
respectively to control the speed of a BLDC motor.The
parameters used are given in Table I which is same for both
the controllers. Table II shows a comparison between the
performances of the motor by using both the controllers. In
case of PI controller, the settling time is 0.04 s while in case
of Fuzzy PI controller, the settling time is 0.02 s. The other
performance parameters are extracted from the response
speed curve of both the PI controller and the FLC. From
Table IV, it is clear that the FLC Controller performance is
better than the PI, as the proposed FLC has a very small rise
time, which is 0.01 s.And it is increased in stability without
oscillations and less transients.
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TABLE 4: COMPARATIVE STUDY OF PI AND Fuzz y LOGIC CONTROLLER
S.No.
1
2
3
4
5
Parameter
Rise T ime
Settling T ime
Steady State Error
Start Up Current
Peak Overshoot Value
PI Controller
0.001 s
0.04 s
0.04%
2A
Less
F uzzy Logic
Controller
0.01 s
0.02 s
0.01%
1.8A
N il
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