CHAPTER -1

INTRODUCTION
1. Introduction:
Permanent magnet brushless DC motors (PMBLDC) find wide applications in
industries due to their high power density and ease of control. These motors generally
controlled by using a three-phase power semiconductor bridge. For starting and the providing
proper commutation, sequence to turn on the power devices in the inverter bridge the rotor
position sensors required. Based on the rotor position, the power devices are commutated
sequentially every 60 degrees. To achieve desired level of performance the motor requires
suitable speed controllers. In case of permanent magnet motors, usually speed control is
achieved by using proportional-integral (PI) controller. Although conventional PI controllers
are widely used in the industry due to their simple control structure and ease of
implementation, these controllers pose difficulties where there are some control complexity
such as nonlinearity, load disturbances and parametric variations. Moreover, PI controllers
require precise linear mathematical models.

This thesis presents a Fuzzy Logic Controller (FLC) for speed control of a BLDC by
using. The Fuzzy Logic (FL) approach applied to speed control leads to an improved dynamic
behaviour of the motor drive system and an immune to load perturbations and parameter
variations. The FLC is design using based on a simple analogy between the control surfaces
of the FLC and a given Proportional-Integral controller (PIC) for the same application. Fuzzy
logic control offers an improvement in the quality of the speed response, compared to PI
control. This work focuses on investigation and evaluation of the performance of a permanent
magnet brushless DC motor (PMBLDC) drive, controlled by PI, and Fuzzy logic speed
controllers. The Controllers are for the PMBLDC motor drive simulated using MATLAB soft
ware package. Further, the PI controller has been implementing on an experimental BLDC
motor set up.

1.1. Background:

Brushless dc (BLDC) motors are preferred as small horsepower control motors due to
their high efficiency, silent operation, compact form, reliability, and low maintenance.
However, the problems are encountered in these motor for variable speed operation over last
decades continuing technology development in power semiconductors, microprocessors,
adjustable speed drivers control schemes and permanent-magnet brushless electric motor
production have been combined to enable reliable, cost-effective solution for a broad range of
adjustable speed applications.
Household appliances expected to be one of fastest-growing end-product market for
electronic motor drivers over the next five years [4]. The major appliances include clothes
washer’s room air conditioners, refrigerators, vacuum cleaners, freezers, etc. Household
appliance have traditionally relied on historical classic electric motor technologies such as
single phase AC induction, including split phase, capacitor-start, capacitor–run types, and
universal motor. These classic motors typically operated at constant-speed directly from main
AC power without regarding the efficiency. Consumers now demand for lower energy costs,
better performance, reduced acoustic noise, and more convenience features. Those traditional
technologies cannot provide the solutions.

1.2. Typical BLDC motor applications

BLDC motors find applications in every segment of the market. Such as, appliances,
industrial control, automation, aviation and so on. We can categorize the BLDC motor
control into three major types such as
• Constant load
• Varying loads
• Positioning applications

1.2.1. Applications with Constant Loads

These are the types of applications where a variable speed is more important than
keeping the accuracy of the speed at a set speed. In these types of applications, the load is
directly couple to the motor shaft. For example, fans, pumps and blowers come under these
types of applications. These applications demand low-cost controllers, mostly Operating in
open loop.
1.2.2. Applications with Varying Loads

These are the types of applications where the load on the motor varies over a speed
range. These applications may demand high-speed control accuracy and good dynamic
responses. In home appliances, washers, dryers and compressors are good examples.
In Automotive, fuel pump control, electronic steering control, engine control and
electric vehicle control are good examples of these. In aerospace, there are a number of
applications, like centrifuges, pumps, robotic arm controls, gyroscope controls and so on.
These applications may use speed feedback devices and may run in semi-closed loop or in
total closed loop. These applications use advanced control algorithms, thus complicating the
controller. In addition, this increases the price of the complete system.
1.2.3. Positioning Applications
Most of the industrial and automation types of application come under this category.
The applications in this category have some kind of power transmission, which could be
mechanical gears or timer belts, or a simple belt driven system. In these applications, the
dynamic response of speed and torque are important. In addition, these applications may have
frequent reversal of rotation direction. A typical cycle will have an accelerating phase, a
constant speed phase and a deceleration and positioning phase. The load on the motor may
vary during all of these phases, causing the controller to be complex. These systems mostly
operate in closed loop.
There could be three control loops functioning simultaneously: Torque Control Loop,
Speed Control Loop and Position Control Loop. Optical encoder or synchronous resolves
used for measuring the actual speed of the motor. In some cases, the same sensors used to get
relative position information. Otherwise, separate position sensors may used to get absolute
positions. Computer Numeric Controlled (CNC) machines are a good example of this.

1.3. A Comparison of BLDC with conventional DC motors

In a conventional (brushed) DC-motor, the brushes make mechanical contact with a
set of electrical contacts on the rotor (called the commutator), forming an electrical circuit
between the DC electrical source and the armature coil-windings. As the armature rotates on
axis, the stationary brushes meet different sections of the rotating commutator. The
commutator and brush-system form a set of electrical switches, each firing in sequence, such
that electrical-power always flows through the armature-coil closest to the stationary stator
(permanent magnet).
In a BLDC motor, the electromagnets do not move; instead, the permanent magnets
rotate and the armature remains static. This gets around the problem of how to transfer
current to a moving armature. In order to do this, an intelligent electronic controller replaces
the commutator assembly. The controller performs the same power-distribution found in a
brushed DC-motor, but using a solid-state circuit rather than a commutator. BLDC motors
have many advantages over DC motors. A few of these are:
 High dynamic response
 High efficiency
 Long operating life
 Noiseless operation
 Higher speed ranges
BLDC's main disadvantage is higher cost that arises from two issues. First, BLDC
 motors require complex electronic speed controllers to run. Brushed DC-motors can be
regulated by a comparatively trivial variable resistor (potentiometer or rheostat), which is
inefficient but also satisfactory for cost-sensitive applications.

1.4. Literature Review:

Recent research [1]-[2] has indicated that the permanent magnet motor drives, which
include the permanent magnet synchronous motor (PMSM) and the brushless dc motor
(BDCM) could become serious competitors to the induction motor for servo applications. The
PMSM has a sinusoidal back EMF and requires sinusoidal stator currents to produce constant
torque while the BDCM has a trapezoidal back EMF and requires rectangular stator currents
to produce constant torque. Some confusion exists, both in the industry and in the university
research environment, as to the correct models that should be use in each case. The PMSM is
very similar to the standard wound rotor synchronous machine except that the PMSM has no
damper windings and excitation provided by a permanent magnet instead of a field winding.
Hence the d, q model of the PMSM can be derived from the well-known [4] model of the
synchronous machine with the equations of the damper windings and field current dynamics
removed.

As is well known, the transformation of the synchronous machine equations from the
abc phase variables to the d, q variables forces all sinusoidal varying inductances in the abc
frame to become constant in the d, q frame. In the BDCM motor, since the back EMF is non-
sinusoidal, the inductances do not vary sinusoidal in the abc frame and it does not seem
advantageous to transform the equations to the d, q frame since the inductances will not be
constant after transformation. Hence, it is propose to use the abc phase variables model for
the BDCM. In addition, this approach in the modelling of the BDCM allows a detailed
examination of the machine’s torque behaviour that would not be possible if any simplifying
assumptions made.
The d, q model of the PMSM used to examine the transient behaviour of a high-
performance vector controlled PMSM servo drive [5]. In addition, the abc phase variable
model been used to examine the behaviour of a BDCM speed servo drive [6]. Application
characteristics of both machines been presented in [7]. The purpose of this paper is to present
these two models together and to show that the d, q model is sufficient to study the PMSM in
detail while the abc model should use in order to study the BDCM.
1.5. A brief review on control of BLDC motor:
The ac servo has established itself as a serious competitor to the brush-type dc servo for
industrial applications. In the fractional-to-30-hp range, the available ac servos include the
induction, permanent magnet synchronous, and brushless dc motors (BDCM) [8]. The BDCM
has a trapezoidal back EMF, and rectangular stator currents needed to produce a constant
electric torque. Typically, Hysteresis or pulse width-modulated (PWM) current controllers
used to maintain the actual currents flowing into the motor as close as possible to the
rectangular reference values. Although some steady-state analysis has been done [9], [10], the
modelling, detailed simulation, and experimental verification of this servo drive has been
neglected in the literature.

It shown that, because of the trapezoidal back EMF and the consequent no sinusoidal
variation of the motor inductances with rotor angle, a transformation of the machine
equations to the well-known d, q model is not necessarily the best approach for modelling and
simulation. Instead, the natural or phase variable approach offers many advantages.

Because the controller must direct the rotor rotation, the controller needs some means
of determining the rotor's orientation/position (relative to the stator coils.) Some designs use
Hall Effect sensors or a rotary encoder directly measure the rotor's position. The controller
contains three bi-directional drivers to drive high-current DC power, which controlled by a
logic circuit. Simple controllers employ comparators to determine when the output phase
should advanced, while more advanced controllers employ a microcontroller to manage
acceleration, control speed and fine-tune efficiency. Controllers that sense rotor position
based on back-EMF have extra challenges in initiating motion because no back-EMF is
produce when the rotor is stationary.

The design of the BLDCM servo system usually requires time consuming trial and
error process, and fail to optimize the performance. In practice, the design of the BLDCM
drive involves a complex process such as model, devise of control Scheme, simulation and
parameters tuning. In a PI controller been proposed for BLDCM. The PI controller can be
suitable for the linear motor control. However, in practice, many non- linear factors imposed
by the driver and load, the PI controller cannot be suitable for non-linear system.

1.6. Problem statement

To achieve desired level of performance the motor requires suitable speed controllers.
In case of permanent magnet motors, usually speed control is achieved by using proportional-
integral (PI) controller. Although conventional PI controllers are widely used in the industry
due to their simple control structure and ease of implementation, these controllers pose
difficulties where there are some control complexity such as nonlinearity, load disturbances
and parametric variations. Moreover, PI controllers require precise linear mathematical
models. As the PMBLDC machine has nonlinear model, the linear PI may no longer be
suitable.
The Fuzzy Logic (FL) approach applied to speed control leads to an improved
dynamic behaviour of the motor drive system and an immune to load perturbations and
parameter variations. Fuzzy logic control offers an improvement in the quality of the speed
response. Most of these controllers use mathematical models and are sensitive to parametric
variations. These controllers are inherently robust to load disturbances. Besides, fuzzy logic
controllers can easily implement.

1.7. Thesis organization

This thesis contains seven chapters describing the modelling and control approach of a
permanent magnet brushless dc motor organized as follows

Chapter 2 discussed principal brushless dc motor, brushless dc motor operation with

inverter with 120 degree angle operation and PWM voltage and current operation, Hall
position sensors, mathematical modelling of machine in state space form.
Chapter 3 describes the design of PI speed controller, PI speed controller of brushless
dc motor and modelling of PI control of brushless dc motor drive elements are references
current generator, back EMF function modelling and Hysteresis current controller.

Chapter 4 describes the fuzzy logic control structure and modelling of fuzzy speed
control of brushless dc motor with triangular membership function.
Chapter 5 describes the modelling of NPC converter and reducing ripple.

Chapter 6 describes the results and discussion for the PI speed control performance
and fuzzy speed control performance with simulation results discussed.
 CHAPTER -2
INTRODUCTION TO BLDC MOTOR DRIVE
2.1. Brushless dc motor background
BLDC motor drives, systems in which a permanent magnet excited synchronous
motors fed with a variable frequency inverter controlled by a shaft position sensor. There
appears a lack of commercial simulation packages for the design of controller for such BLDC
motor drives. One main reason has been that the high software development cost incurred is
not justified for their typical low cost fractional/integral kW application areas such as NC
machine tools and robot drives; even it could imply the possibility of demagnetizing the rotor
magnets during commissioning or tuning stages. Nevertheless, recursive prototyping of both
the motor and inverter may be involved in novel drive configurations for advance and
specialized applications, resulting in high developmental cost of the drive system. Improved
magnet material with high (B.H), product also helps push the BLDC motors market to tens of
kW application areas where commissioning errors become prohibitively costly. Modelling is
therefore essential and may offer potential cost savings.
A brushless dc motor is a dc motor turned inside out, so that the field is on the rotor
and the armature is on the stator. The brushless dc motor is actually a permanent magnet ac
motor whose torque-current characteristics mimic the dc motor. Instead of commutating the
armature current using brushes, electronic commutation is used. This eliminates the problems
associated with the brush and the commutator arrangement, for example, sparking and
wearing out of the commutator-brush arrangement, thereby, making a BLDC more rugged as
compared to a dc motor. Having the armature on the stator makes it easy to conduct heat
away from the windings, and if desired, having cooling arrangement for the armature
windings is much easier as compared to a dc motor.

Fig 2.1 Cross-section view of a brushless dc motor

 In effect, a BLDC is a modified PMSM motor with the modification being
that the back-EMF is trapezoidal instead of being sinusoidal as in the case of PMSM.
The “commutation region” of the back-EMF of a BLDC motor should be as small as
possible, while at the same time it should not be so narrow as to make it difficult to
commutate a phase of that motor when driven by a Current Source Inverter. The flat
constant portion of the back-EMF should be 120° for a smooth torque production.
The position of the rotor can sensed by using an optical position sensors and
its associated logic. Optical position sensors consist of phototransistors (sensitive to
light), revolving shutters, and a light source. The output of an optical position sensor
is usually a Logical signal.

2.2. Principle operation of Brushless DC (BLDC) Motor

A brush less dc motor defined as a permanent synchronous machine with rotor
position feedback. The brushless motors generally controlled using a three-phase
power semiconductor bridge. The motor requires a rotor position sensor for starting
and for providing proper commutation sequence to turn on the power devices in the
inverter bridge. Based on the rotor position, the power devices are commutated
sequentially every 60 degrees. Instead of commutating the armature current using
brushes, electronic commutation used for this reason it is an electronic motor. This
eliminates the problems associated with the brush and the commutator arrangement,
for example, sparking and wearing out of the commutator brush arrangement, thereby,
making a BLDC more rugged as compared to a dc motor.

Fig.2.2.Basic block diagram of BLDC motor

The basic block diagram brushless dc motor as shown Fig.2.1.The brush less
dc motor consist of four main parts power converter, permanent magnet-synchronous
machine (PMSM) sensors, and control algorithm. The power converter transforms
power from the source to the PMSM, which in turn converts electrical energy to
mechanical energy. One of the salient features of the brush less dc motor is the rotor
position sensors ,based on the rotor position and command signals which may be a
torque command ,voltage command ,speed command and so on the control algorithms
determine the gate signal to each semiconductor in the power electronic converter.
The structure of the control algorithms determines the type of the brush less dc
motor of which there are two main classes voltage source based drives and current
source based drives. Both voltage source and current source based drive used with
permanent magnet synchronous machine with either sinusoidal or non-sinusoidal back
EMF waveforms .Machine with sinusoidal back EMF (Fig.2.3) may controlled to
achieve nearly constant torque. However, machine with a non-sinusoidal back EMF
(Fig.2.4) offer reduces inverter sizes and reduces losses for the same power level.

Fig.2.3.Trapezoidal back EMF of three phase BLDC motor

Fig.2.4.Sinusoidal phase back EMF of BLDC motor

2.3. BLDC drives operation with inverter
It is an electronic motor and requires a three-phase inverter in the front end as
shown in Fig. 2.5. In self-control mode, the inverter acts like an electronic
commutator that receives the switching logical pulse from the absolute position
sensors. In addition, the drive known as an electronic commutated motor.
The inverter can operate in the following two modes.
 Angle switch-on mode
 Voltage and current control PWM mode

Fig.2.5.Brushless dc motor drive system

2.3.1. 2 π /3 Angle switch-on mode:
Inverter operation in this mode with the help of the wave from shown on
Fig.2.6.The 6 switches of the inverter (T1 − T6 ) operate in such way to place the input
dc current Id symmetrical for angle at the centre of each phase voltage wave. The
angle α is 120 deg shown is the advance angle of current wave with respect to voltage
wave in the case α is zero. It can see that any instant, two switches are on, one in the
upper group and anther is lower group. For example instant t 1, T1 and T6 are on when
the supply voltage Vdc and current Id is place across the line ab (phase A and phase B
in series) so that Id is positive in phase A. However, negative in phase B then after
π /3 interval (the middle of phase A) T6 is turned off and T2 is turned on but T1
continuous conduction of the full 2 π /3 angle. This switching commutates -Id from
phase B to phase C while phase A carry +Id. The conduction pattern changes every
π /3 angle indication switching modes in full cycle. The absolute position sensor
dictates the switching or commutation of devices at the precise instants of wave. The
inverter operates as a rotor position sensitive electronic commutator.

2.3.2. Voltage and current control PWM mode

In the previous mode, the inverter switches controlled to give commutator
function only when the devices were sequentially ON, OFF 2 π /3 Angle duration in
addition to the commutator function. It is possible to control the switches in PWM
chopping mode for controlling voltage and current continuously at the machine
terminal. There are essentially two chopping modes, current controlled operation of
the inverter. There are essentially two chopping modes feedback (FB) mode and
freewheeling mode. In both these modes, devices turned on and off on duty cycle
basis to control the machine average current I AV and the machine average voltage
Vavg.

Fig.2.6. Back-EMFs, current waveforms and Hall position sensors for BLDC
2.4. Rotor position sensors
 Hall Effect sensors provide the portion of information need to synchronize the
motor excitation with rotor position in order to produce constant torque. It detects the
change in magnetic field. The rotor magnets used as triggers the hall sensors. A signal
conditioning circuit integrated with hall switch provides a TTL-compatible pulse with
sharp edges. Three hall sensors placed 120 degree apart mounted on the stator frame.
The hall sensors digital signals used to sense the rotor position.

Fig.2.7. Hall position sensors

2.5. Machine Dynamic Model

The BLDCM has three stator windings and a permanent magnet rotor on the
rotor. Rotor induced currents can be neglected due to the high resistivity of both
magnets and stainless steel. No damper winding are modelled the circuit equation of
the three windings in phase variables are obtained. In eq. 2.1
v as Rs 0 0 i a L Lab Lac i a e a

[ ][
v cs 0 0 R s ic ][ ] [
v bs = 0 R s 0 i b +
d aa
L
dt ba
Lbb Lbc i b + e b
Lca Lcb L cc i c ec ][ ] [ ] (2.1)

Where
vas , vbs and, vcs are the stator phase voltages
Rs is the stator resistance per phase
 ia, ib, and ic are the stator phase currents
Laa, Lbb, and Lcc are the self inductance of phases a, b and c
Lab, Lbc, and Lca are the mutual inductances between phase a, b and c.
ea, eb and ec are the phase back electromotive forces.
It is assumed that resistance of all the winding are equal. It also has been
assumed that if there is no change in the rotor reluctance with angle because of a no
salient rotor and then
Laa  Lbb  Lcc  L (2.2)
Lab  Lba  Lac  Lca  Lbc  Lcb  M (2.3)
 CHAPTER 3
DESIGN OF A PI SPEED CONTROLLER SCHEME
3.1. PI speed controller design
A proportional integral-derivative is control loop feedback mechanism used in
industrial control system. In industrial process a PI controller attempts to correct that
error between a measured process variable and desired set point by calculating and
then outputting corrective action that can adjust the process accordingly.
The PI controller calculation involves two separate modes the proportional
mode, integral mode. The proportional mode determine the reaction to the current
error, integral mode determines the reaction based recent error. The weighted sum of
the two modes output as corrective action to the control element. PI controller is
widely used in industry due to its ease in design and simple structure. PI controller
algorithm can implemented as
t

Output (t) =Kp e(t) +KI ∫ e ( τ ) dτ (3.1)

0

Where e(t) = set reference value – actual calculated

3.2. PI speed control of the BLDC motor
In Fig. 3.1 describes the basic building blocks of the PMBLDCM drive. The
drive consists of speed controller, reference current generator, PWM current
controller, position sensor, the motor and IGBT based current controlled voltage
source inverter (CC-VSI). The speed of the motor compared with its reference value,
the speed error is processed, and resulting error estimated in proportional integral (PI)
speed controller.
e (t) = ω ref −ω m ( t ) (3.2)
ω m ( t ) Is compared with reference speed ω ref nth sampling instant as
T ref ( t )=T ref ( t−1 ) + K p [ e ( t )−e ( t−1 ) ]+ K I e (t) (3.3)
Where KI and Kp are the gains of PI speed controller
The output of this controller considered as the reference torque. A limit put on
the speed controller output depending on permissible maximum winding currents. The
reference current generator block generates the three phase reference currents i a ib, ,ic
using the limited peak current magnitude decided by the controller and the position
sensor.
 Fig.3.1.PI speed controller of the BLDCM drive
The reference currents have the shape of quasi-square wave in phase with
respective back EMF develops constant unidirectional torque. The PWM current
controller regulates the winding currents i a, ib & ic with in the small band around. The
reference currents ia, ib & ic the motor currents are compared with the reference
currents and the switching commands are generated to drive the inverter devices.

3.3. Modelling of speed control of BLDC motor drive system

The drive system considered here consists of PI speed controller, the reference
current generator, PWM current controller, PMBLDC motor and an IGBT inverter.
All these components modelled and integrated for simulation in real time conditions.
3.1.1. Reference Current Generator
The magnitude of the three phase current i ref is determine by using reference torque
Tref
i ref =T ref / Kt (3.4)
Where
Kt is the torque constant.
Kt Depending on the rotor position, the reference current generator block
generates three-phase reference currents (ia, ib & ic ) by taking the value of Reference

current magnitude as iref .The reference currents are fed to the PWM current
controller. The reference current for each phase i a, ib and ic are functions of the rotor
positions. These reference currents fed to the PWM current controller Rotor position
signal and Reference currents shown in Table.3.1.
Table 3.1 Rotor position signal and reference currents

Rotor position
i ¿a i ¿b i ¿c
θr

0-60 i ref −i ref 0

60-120 i ref 0 −i ref

120-180 0 i ref −i ref

180-240 −i ref i ref 0

240-300 −i ref 0 i ref

300-360 0 −i ref i ref

3.3.2. Hysteresis current controller

The Hysteresis current controller contributes to the generation of the switching
signals for the inverter. hysteresis-band PWM is basically an instantaneous feedback
current control method of PWM where the actual current continually tracks the
command current continually tracks the command current within hyssteresis-
band.Fig.3.2 explains the operation principle of hysteresis-band PWM for half-bridge
inverter. The control circuit generates the sine reference current and it compared with
actual phase current wave.

Fig.3.2. the structure of PWM current controller

 The current exceed upper band limit the upper switch is off and lower switch
is on. As the current exceed lower band limit upper switch is on and lower switch is
off like this control of the other phase going on
The switching logic formulated as given below
If i a < ( i a−h b ) switch 1 ON and switch 4 OFF S A =1
If i a < ( i a +hb ) switch 1 OFF and switch 4 ON S A =0
If i a < ( i b−h b ) switch 3 ON and switch 6 OFF S B=1
If i a < ( i b +hb ) switch 3 OFF and switch 6 ON S B=0
If i a < ( i c −hb ) switch 5 ON and switch 2 OFF SC =1
If i a < ( i c + hb ) switch 5 OFF and switch 2 ON SC =0
Where, hb is the hysteresis band around the three phase’s references currents,
according to above switching condition of the inverter output voltage are given below
1
v a= [2 S A −S B −S C ]
3
1
v b= [−S A +2 S B−S C ]
3
1
v c = [−S A −S B + 2 SC ]
3
(3.5)
 CHAPTER -4
FUZZY LOGIC CONTROL SCHEME
4.1 Introduction to FLC
Fuzzy logic has rapidly become one of the most successful of today’s
technology for developing sophisticated control system. With it, aid complex
requirement so may implemented in amazingly simple, easily minted and inexpensive
controllers. The past few years have witnessed a rapid growth in number and variety
of application of fuzzy logic. It ranges from consumer products such as cameras,
camcorder, washing machines and microwave ovens to industrial process control,
medical instrumentation and decision support systems. Many decision-making and
problem solving tasks are too complex to understand quantitatively however, people
succeed by using knowledge that is imprecise rather than precise. Fuzzy logic is all
about the relative importance of precision. Fuzzy logic has two different meanings in
a narrow senses, fuzzy logic is a logical system, which is an extension of multi-valued
logic but in wider sense fuzzy logic, is synonymous with the theory of fuzzy sets.
Fuzzy set theory is originally introduced by Lotfi Zadeh in the 1960’s [15] resembles
approximate reasoning in it use of approximate information and uncertainty to
generate decisions.
Several studies show, both in simulations and experimental results, that Fuzzy
Logic control yields superior results with respect to those obtained by conventional
control algorithms thus, in industrial electronics the FLC control has become an
attractive solution in controlling the electrical motor drives with large parameter
variations like machine tools and robots. However, the FL Controllers design and
tuning process is often complex because several quantities, such as membership
functions, control rules, input and output gains, etc must be adjusted. The design
process of a FLC can simplify if some of the mentioned quantities obtained from the
parameters of a given Proportional-Integral controller (PIC) for the same application.

4.2 Motivations for choosing fuzzy logic controller (FLC)

 Fuzzy logic controller can model nonlinear systems. The design of
conventional control system essential is normally based on the mathematical
model of plant if an accurate mathematical model is available with known
parameters it can be analyzed for example by bode plots or nyquist plot and
 controller can be designed for specific performances such procedure is time
consuming.
 Fuzzy logic controller has adaptive characteristics.
The adaptive characteristics can achieve robust performance to system with
uncertainty parameters variation and load disturbances.
4.3. Fuzzy logic controller (FLC)
Fuzzy logic expressed operational laws in linguistics terms instead of
mathematical equations. Many systems are too complex to model accurately, even
with complex mathematical equations. Therefore, traditional methods become
infeasible in these systems. However, fuzzy logics linguistic terms provide a feasible
method for defining the operational characteristics of such system.
Fuzzy logic controller can considered as a special class of symbolic controller.
The configuration of Fuzzy Logic Controller block diagram shown in Fig.4.1

Fig.4.1. Structure of fuzzy logic controller

The fuzzy logic controller has three main components
1. Fuzzification
2. Fuzzy inference
3. Defuzzification

4.3.1 Fuzzification
The following functions:
1. Multiple measured crisp inputs first must be mapped into fuzzy membership
function this process is called fuzzification.
 2. It performs a scale mapping that transfers the range of values of input
variables into corresponding universes of discourse.
3. Performs the function of fuzzification that converts input data into suitable
linguistic values, which may be view as labels of fuzzy sets.

Fuzzy logic linguistic terms are often express in the form of logical implication,
such as if-then rules. These rules define a range of values known as fuzzy member
ship functions. Fuzzy membership function may be in the form of a triangular, a
trapezoidal, a bell (as shown in Fig.4.2) or another appropriate from.
The triangular membership function defined in 4.1. Triangle membership function
limits defined by Val1, Val2 and Val3.

0 , α ≤ v al 1

{
α−v al 1
, v al 1 ≤α ≤ v al2
v −v
μa ( α )= al 2 al 1
v al 3−α
, v ≤ α ≤ v al3
v al3 −v al 2 al 2
0 , α ≥ v al 3

(4.1)
Trapezoid membership function defined in (4.2). Trapezoid membership function
limits defined by Val1, Val2, Val3 and Val4.

0 , α ≤ v al 1

{
α −v al1
, v al 1 ≤ α ≤ v al 2
μa ( α )= v al 2−v al 1
1, v al2 ≤ α ≤ v al 3
v al 4−α
, v ≤ α ≤ v al 4
v al 4−v al3 al 3

(4.2)
The bell membership functions defined by parameters Xp, w and m as follows
1
μ ( ui ) = 2m
u −Xp
[
1+ i
w ]
(4.3)
Where Xp the midpoint and w is the width of the Bell function m≥1, and
describe the convexity of the bell function.
 Fig.4.2.a) Triangle, b) Trapezoid and c) Bell membership function
The inputs of the fuzzy controller are express in several linguist levels. As
shown in Fig.4.3 these levels can be described as Positive big (PB), Positive medium
(PM), Positive small (PS) Negative small (NS), Negative medium (NM), Negative big
(NB) or in other levels. Each level is describ by fuzzy set.

Fig.4.3. Seven levels of fuzzy membership function

4.3.2. Fuzzy inference:
Fuzzy inference is the process of formulating the mapping from a given input
to an output using fuzzy logic. The mapping then provides a basis from which
decisions can make, or patterns discerned. There are two types of fuzzy inference
systems that can be implemented in the Fuzzy Logic Toolbox: Mamdani-type and
Sugeno-type. These two types of inference systems vary somewhat in the way outputs
are determined.
A fuzzy inference system has been successfully applied in fields such as
automatic control, data classification, decision analysis, expert systems and computer
vision. Because of its multidisciplinary nature, fuzzy inference systems are associated
with a number of names, such as fuzzy-rule-based systems, fuzzy expert systems,
fuzzy modelling, fuzzy associative memory, and fuzzy logic controllers and simply
(and ambiguously) fuzzy.
Mamdani’s fuzzy inference method is the most commonly seen fuzzy
methodology. Mamdani’s method was among the first control systems built using
fuzzy set theory. It proposed in 1975 by Ebrahim Mamdani [Mam75] as an attempt to
control a steam engine and boiler combination by synthesizing a set of linguistic
control rules obtained from experienced human operators. Mamdani’s effort was
based on Lotfi Zadeh’s 1973 paper on fuzzy algorithms for complex systems and
decision processes [Zad73].
The second phase of the fuzzy logic controller is its fuzzy inference where the
knowledge base and decision-making logic reside .The rule base and database from
the knowledge base. The database contains the description of the input and output
variables. The decision making logic evaluates the control rules .the control-rule base
can be developed to relate the output action of the controller to the obtained inputs.
4.3.3. Defuzzification:
The output of the inference mechanism is fuzzy output variables. The fuzzy
logic controller must convert its internal fuzzy output variables into crisp values so
that the actual system can use these variables. This conversion called as
defuzzification. One may perform this operation in several ways. The commonly used
control defuzzification strategies are
a) The max criterion method (MAX)
The max criterion produces the point at which the membership
function of fuzzy control action reaches a maximum value.
b) The height method
The centroid of each membership function for each rule first evaluated.
The final output U0 then calculated as the average of the individual
 centroids, weighted by their heights as follows:
n

∑ u i ∝(ui )
U 0 = i=1n
∑ ∝(ui)
i=1

(4.4)
c) The centroid method or center of area method (COA)
The widely used centroid strategy generates the center of gravity of
area bounded by the Membership function cure

ý=
∫ ∝ ( y ) . ydy
∫ ∝ ( y ) dy
(4.5)
4.4. Fuzzy logic control of the BLDC motor
The fuzzy logic controller applied to the speed loop by replacing the classical
polarization index (PI) controller. The fuzzy logic controlled BLDCM drive system
block diagram is shown in Fig 4.4.

Fig.4.4. Fuzzy speed control block diagram of the BLDC motor.

The input variable is speed error (E), and change in speed error (CE) is
calculated by the controller with E .The output variable is the torque component of the
reference (iref ) where iref is obtained at the output of the controller by using the change
in the reference current.
The triangular shaped functions are chosen as the membership functions due
to the resulting best control performance and simplicity. The membership function for
the speed error and the change in speed error and the change in torque reference
current are shown in Fig. 4.5 .For all variables seven levels of fuzzy membership
function are used .Table .II show the 7×7 rule base tables that was used in the system.
Table.4.1. 7 × 7 Rule base tables used in the system
e/ce NB NM NS ZO PS PS PB
NB NB NB NB NB NM NS ZO
NM NB NB NB NM NS ZO PS
NS NB NB NM NS ZO PS PM
ZO NB NM NS ZO PS PM PB
PS NM NS ZO PS PM PB PB
PM NS ZO PS PM PB PB PB
PB ZO PS PM PB PB PB PB

The steps for speed controller are as

 BLDCM speed signal sampling
 Calculations of speed error and the change in speed error.
 Determination of fuzzy sets and membership function for the speed error and
Change in speed error.
 Determination of the control action according to fuzzy rule.
 Calculation of the iqs by centre of area defuzzification method.

 Sending the control command to the system after calculation of iqs.

Fig.4.5 (a). Fuzzy membership function for the speed error

 Fig.4.5 (b). Fuzzy membership function for the change in speed error

Fig.4.5(c). Fuzzy membership function for the change in torque reference current
 CHAPTER-5

ANALYSIS OF TORQUE RIPPLE IN BLDCM DRIVE SYSTEM

In most industrial low- and medium-power applications, a conventional 2-
level inverter is a preferred choice. The multilevel-inverter driven ac machines used in
many industrial high power applications due to lower harmonic distortion of the
output currents and operate with reduced dv/dt stress as compared to the 2-level
inverter driven ac machines.
The BLDCM is widely used in more electric aircraft (MEA) applications in a
power range of 100kW to 150kW and dc-bus voltage is from 270Vdc or 540Vdc. The
multilevel converters such as flying capacitor (FC) inverter, cascaded H-bridge
(CHB) inverter, and neutral-point-clamped (NPC) inverter have been widely used in
high-power medium-voltage applications. For FC inverter, the capacitor clamping
requires a large number of expensive and bulky capacitors to clamp the voltage. It
requires a complex control for voltage tracking of capacitors, difficult to control pre-
charging of capacitors to the same voltage level, and operates with poor efficiency.
A 5-level CHB inverter has proposed for harmonics and torque ripple
suppression of BLDCM drive with current and speed closed loop control. This
converter needs galvanically isolated dc source for each of the H-bridge.
In recent years, the MOSFET-based 3-level DCMLIs are preferred to drive
BLDCM for low and medium power applications, which produce low current THD in
the stator windings, smaller voltage steps, reduced switching loss under high
switching frequency and lower common mode voltage amplitude than conventional 2-
level inverter . The 3-level DCMLI topology provides a significant reduction in ripple
current for low inductance BLDCM without need for very high switching frequency
than 2-level inverter. In addition, it operates with a lower number of DC sources and
power semiconductor devices than FC multilevel inverter and CHB multilevel
inverter.
In this project, a novel converter topology proposed to reduce the torque ripple
of the BLDCM drive system. The proposed converter is composed a modified SEPIC
converter and a MOSFET-based 3-level DCMLI.
The modified SEPIC converter operates with high static gain and less
switching voltage stress than classical DC-DC converters. Hence, the modified SEPIC
converter used in this proposed torque ripple suppression circuit and the duty cycle is
adjusted to obtain the desired dc-bus voltage based on the spinning speed of the
BLDCM. The equivalent circuit of BLDCM drive system with conventional 2-level
inverter and BLDCM shown in Fig.5.1.

Fig.5.1.Equivqlent model of 2-level inverter

The 3-level DCMLI used for further reduction of the current ripple and as well
as the resultant torque ripple. The MOSFET-based voltage selector circuit used to
apply regulated dc-bus voltage for efficient commutation torque ripple suppression.
Simulation and experimental results show that the proposed converter topology with
the dc-bus voltage selector circuit significantly reduces the torque ripple during the
commutation interval In order to minimize the commutation torque ripple of BLDCM,
the influence of phase current slew rates of rising phase and decaying phase during
the commutation period analyzed.
A six-step voltage source inverter employed for the control of BLDCM. For
torque ripple analysis, the current transition from phase v1 to v2 during commutation
period is considered. At the beginning of commutation period, MOSFET T1 is turned
off to de-energize the phase v1 and MOSFET T2 is turned on to energize the phase
v2, with phase v3 remaining in the conduction state.
In 120 degree conduction method, two power MOSFETs conduct at each 60
electrical degrees, one MOSFET from the upper arm and other MOSFET from the
lower arm. Before the commutation period, the MOSFETs T1 and T2 are turn on and
current through the circuit builds up as shown in Fig. 5.2(a). At the start of
commutation period, T1 is switch off, and then freewheeling diode D4 starts to
conduct due to stored energy in the inductor as shown in Fig. 5.2(b). After the
commutation process, the MOSFETs T2 and T3 continue to conduct as shown
Fig.5.2(c). The current transition from phase v1 to v2 during the commutation interval
at different speed conditions are shown in Fig.5.2. The difference in current slew rates
between the incoming phase and outgoing phase generate torque ripple.

Fig.5.2 Phase current behaviour at various speed conditions during

commutation interval
A system diagram of a proposed new converter topology for BLDCM drive
system based on a 3-level DCMLI and a modified SEPIC converter is shown in Fig.
5.3.

Fig.5.3. Proposed converter topology with a dc-bus voltage selector circuit for
BLDCM
 In this topology, the 3-level DCMLI is proposed to reduce current ripple, and
modified SEPIC converter is included to adjust the dc-bus voltage based on the
rotational speed of the BLDCM. The dc-bus voltage selector circuit constructed with
power MOSFETs (S1, S2, S3, and S4). It is used to select the desired dc-bus voltage
for significant torque ripple reduction during commutation interval. The MOSFET-
based 3-level DCMLI is operated at a switching of 80 kHz, which provides significant
torque ripple suppression than the conventional 2-level inverter. In this 3-level
DCMLI, the dc-bus voltage is divided into 3-levels by the capacitors C5 and C6. To
obtain the desired commutation voltage, the duty cycle of the modified SEPIC
converter can be adjusted during the non-commutation period to maintain Vdc = 8Em.
At the start of commutation period, voltage selector circuit for significant torque
ripple suppression instantly applies the regulated voltage from the modified SEPIC
converter. Equation 5.1 represents the mathematical model of BLDCM.
v1 R 0 0 i1 L 0 0 d i1 e 1 un

[ ] [ ][ ] [ ] [ ] [ ] [ ]
v2 = 0 R
v3 0 0
0 i2 + 0
R i3 0
L 0
0 L
i + e 2 + un
dt 2
i3 e 3 un

(5.1)
The phase current behaviour of 3-level DCMLI-fed BLDCM with different
operating speed Based on the torque ripple analysis, a novel converter topology is
proposed with modified SEPIC converter, which regulates the dc-bus voltage closer to
8Em based on the measurement of the rotational speed of BLDCM. The dc-bus
voltage selector circuit applies regulated dc-bus voltage during the commutation
period, which significantly diminishes the commutation torque ripple.
Block diagram of PWM controller for 3-level DCMLI.
 CHAPTER -6
MATLAB & SIMULATION RESULTS

The proposed three levels NPC fed BLDC motor is shown in figure 6.1.

Fig.6.1.NPC fed BLDC motor

Fig.6.1 (a). BLDCM fed by 2-level inverter.
Fig 6.1(b). BLDCM fed by 3-level DCMLI
Fig.6.1(c). BLDCM fed by 2 level inverter with sepic converter and a switch
circuit
6.1 Simulation Results:

Fig.6.2 output waveforms of current and torque for BLDCM fed by 2-level
inverter
Fig.6.3 output waveforms of current and torque for BLDCM fed by 3-level
DCMIL
Fig.6.4 output waveforms of current and torque for BLDCM fed by 2 level
inverter with sepic converter and a switch circuit
Fig.6.5 output waveforms of current and torque for BLDCM fed by proposed
topology
Fig.6.6. Simulated waveforms of phase current and torque at 1000 rpm and
0.825 Nm with 5 kHz switching frequency.(a) BLDCM fed by 2-level inverter.
(b) BLDCM fed by 3-level DCMLI. (c) BLDCM fed by 2-level inverter with
SEPIC converter and a switch selection circuit. (d) BLDCM fed by proposed
topology.

Fig.6.6(a) BLDCM fed by 2-level inverter.

Fig.6.6(b). BLDCM fed by 3-level DCMLI.
Fig.6.6(c).BLDCM fed by 2-level inverter with SEPIC converter and
a switch selection circuit.
Fig.6.6(d).BLDCM fed by proposed topology.
Fig. 6.7. Simulated waveforms of phase current and torque at
6000 rpm and 0.825 Nm with 5 kHz switching frequency. (a)
BLDCM fed by 2-level inverter. (b) BLDCM fed by 3-level
DCMLI. (c) BLDCM fed by 2-level inverter with SEPIC converter
and a switch selection circuit. (d) BLDCM fed by proposed
topology.

Fig.6.7(a) BLDCM fed by 2-level inverter.

Fig.6.7(b). BLDCM fed by 3-level DCMLI.
Fig.6.7(c).BLDCM fed by 2-level inverter with SEPIC converter and
a switch selection circuit.
Fig.6.7(d).BLDCM fed by proposed topology
Fig. 6.8. Simulated waveforms of phase current and torque at
1000 rpm and 0.825 Nm with 20 kHz switching frequency. (a)
BLDCM fed by 2-level inverter. (b) BLDCM fed by 3-level
DCMLI. (c) BLDCM fed by 2-level inverter with SEPIC converter
and switch a selection circuit. (d) BLDCM fed by proposed
topology.

Fig.6.8(a) BLDCM fed by 2-level inverter.

Fig.6.8(b). BLDCM fed by 3-level DCMLI.
Fig.6.8(c).BLDCM fed by 2-level inverter with SEPIC converter and
a switch selection circuit.
Fig.6.8(d).BLDCM fed by proposed topology
 CONCLUSION
A commutation torque ripple reduction circuit has been proposed using 3-
level DCMLI with modified SEPIC converter and a dc-bus voltage selector
circuit. A laboratory-built drive system has been tested to verify the proposed
converter topology. The suggested dc-bus voltage control strategy is more
effective in torque ripple reduction in the commutation interval. The proposed
topology accomplishes the successful reduction of torque ripple in the
commutation period and experimental results are presented to compare the
performance of the proposed control technique with the conventional 2-level
inverter, 3-level DCMLI, 2-level inverter with SEPIC converter and the
switch selection circuit-fed BLDCM. In order to obtain significant torque
ripple suppression, quietness and higher efficiency, 3-level DCMLI with
modified SEPIC converter and the voltage selector circuit is a most suitable
choice to obtain high-performance operation of BLDCM. The proposed
topology may be used for the torque ripple suppression of BLDCM with the
very low stator winding inductance.
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