Current Research Brushless dc motor (BLDC) is very popular in wide variety of applications. In comparison to conventional dc motor, the brushless dc motor having electronic commutator rather than mechanical commutator, so it is more reliable than the dc motor. BLDC motors are commonly used in high end white goods (refrigerator, washing machine, dish washer etc.), high end pump applications which require higher reliability and efficiency. This paper proposed a new method for sensor less speed control of high-Speed brushless Dc motors with higher reliability of the motor. The sensor less BLDC motor based on back EMF sensing and a rotor position angular detection value. The Nonlinear characteristics of Sensor less BLDC motor analyzed using (TZ) fuzzy model in MATLAB. Simulated Results shows proposed control method superior than conventional controllers.

1. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.45-49
ISSN: 2278-2400
45
Design of Sensor less Sliding-Mode BLDC motor
Speed regulator using Class of Uncertain
Takagi-Sugeno Nonlinear Systems
S. Swapna1
,Joseph Henry2
, K.Siddappa Naidu 3
1
Research scholor,Department of EEE,Veltech university, Chennai, India
2,3
Professor/ EEE,Veltech university, Chennai, India
Email:meswapna1985@gmail.com,joseph_henry@rediffmail.com, ksiddappanaidu@veltechuniv.edu.in
Abstract—Current Research Brushless dc motor (BLDC) is
very popular in wide variety of applications. In comparison to
conventional dc motor, the brushless dc motor having
electronic commutator rather than mechanical commutator, so
it is more reliable than the dc motor. BLDC motors are
commonly used in high end white goods (refrigerator, washing
machine, dish washer etc.), high end pump applications which
require higher reliability and efficiency. This paper proposed a
new method for sensor less speed control of high-Speed
brushless Dc motors with higher reliability of the motor. The
sensor less BLDC motor based on back EMF sensing and a
rotor position angular detection value. The Nonlinear
characteristics of Sensor less BLDC motor analyzed using (T-
Z) fuzzy model in MATLAB. Simulated Results shows
proposed control method superior than conventional
controllers.
Keywords—BLDC, T-Z Fuzzy model, Backemf, nonlinear
I. INTRODUCTION
Recently the increase in energy prices spurs higher demands of
variable speed Permanent Magnet (PM) motors drives. Also,
recent rapid proliferation of motor drives into the automobile
industry, based on hybrid drives, generates a serious demand
for high efficient PM motor drives. The Brushless dc motor
(BLDC) is a Permanent Magnet DC Synchronous motors.
These are used in great amount of industrial sectors because
their architecture is suitable for any safety critical applications,
from a modelling perspective looks exactly like a DC motor
and having linear relationship between current and torque,
voltage and rpm [4,5]. The electromagnets do not move, the
permanent magnets rotate and the armature remains static, the
position sensor and intelligent electronic controller are
required to transfer current to a moving armature, which
performs the same power distribution as a brushed DC motor.
Regular uses of cheap induction motor drives, which have
around 10% lower efficiency than adjustable PM motor drives
for energy saving applications [10]. BLDC motor have many
advantages over brushed DC motors and induction motors,
such as a better speed versus torque characteristics, high
dynamic response, high efficiency and reliability, long
operating life (no brush erosion), noiseless operation, higher
speed ranges and reduction of electromagnetic interference
(EMI). In addition, the ratio of delivered torque to the size of
the motor is higher, making it useful in application where
space and weight are critical factors, PM motors have been
widely used in automotive (hybrid vehicles), computers,
household products and especially in aerospace application
[1]. However, the PM BLDC motors are inherently
electronically controlled and require rotor position information
for proper commutation of currents in its stator windings. But
it is not desirable to use the Hall sensors for high temperature
applications where reliability is of utmost importance because
a sensor failure may cause instability in the control system
[11]. Another major problem is associated with the
conventional controllers that are widely used in the industry
due to its simple control structure and ease of implementation.
But these controllers pose difficulties under the conditions of
nonlinearity, load disturbances and parametric variations [3].
Traditional control systems are based on mathematical models
in which the control system described using one or more
differential equations that define the system response to its
inputs. In many cases, the mathematical model of the control
process may not exist or may be too expensive in terms of
computer processing power and memory and a system based
on empirical rules may be more effective for closed loop speed
control. This paper develops to remove the drawbacks
associated with sensor control and use of traditional
controllers by using zero crossing point (ZCP) based on Back
electromotive force (Back-EMF) sensor less control with
fuzzy logic controller. The sensor less control requires good
reliability and various speed ranges with the high starting
torque for BLDC motor drive system. To satisfy these
requirements, this paper proposes an efficient sensor less
speed control to avoid high energy prices.
A. principle of sensorless BLDC motor control
Brushless DC motor drives have need of rotor position
information for appropriate operation to execute phase
commutation. Position sensors are generally used to provide
the position information for the driver. So this type of position
sensors is not used in sensor less drives. The advantage of
sensor less drives comprises of less hardware cost, increased
system reliability, decreased system size and reduced feedback
units. And also they are free from mechanical and
environmental constraints [2]. Various control methods arises
for sensor less drive, in which a back-EMF is the most cost
effective method to obtain the commutation sequence in the
star wound motors and current sensing provides enough
information to estimate with sufficient rotor position to drive
the motor with synchronous phase currents. BLDC motor
drives that do not require position sensors but it contains
electrical dimensions are called a sensor less drive. The BLDC
motor provides sensor less operation based on the nature of its
excitation intrinsically suggest a low-cost way to take out rotor
position information from motor-terminal voltages. In the
excitation of a 3 phase BLDC motor, apart from the phase-
commutation periods, two of the three phase windings are
functioning at a time and no conducting phase carries in the
back-EMF as shown in Fig. 1. Since back-EMF is zero at
standstill and proportional to speed, the measured terminal

2. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.45-49
ISSN: 2278-2400
46
voltage that has large signal-to-noise ratio cannot detect zero
crossing at low speeds. That is the reason why in all back-
EMF-based sensor less methods the low-speed performance is
limited, and an open-loop starting strategy is required [7, 8].
B. Related Work
In this paper, a brief review of the conventional back EMF
detection will be given first. Then, the proposed novel back
EMF detection will be described. Experiment results
demonstrate the advantages of the novel back EMF sensing
scheme and the sensor less system. Specially, a low cost
mixed-signal microcontroller that is the first commercial one
dedicated for sensor less BLDC drives is developed,
integrating the detection circuit and motor control peripherals
with the standard 8-bit microcontroller core.
C. Conventional Back Emf Detection Schemes
For three-phase BLDC motor, typically, it is driven with six-
step 120 degree conducting mode. At one time instant, only
two out of three phases are conducting current. For example,
when phase A and phase B conduct current, phase C is
floating. This conducting interval lasts 60 electrical degrees,
which is called one step. A transition from one step to another
different step is called commutation. So totally, there are 6
steps in one cycle. In previous chapter, the first step is AB,
then to AC, to BC, to BA, to CA, to CB and then just repeats
this pattern. Usually, the current is commutated in such way
that the current is in phase with the phase Back EMF to get the
optimal control and maximum torque/ampere. The
commutation time is determined by the rotor position. Since
the shape of back EMF indicates the rotor position, it is
possible to determine the commutation timing if the back EMF
is known. In Fig.3.1, the phase current is in phase with the
phase Back EMF. If the zero crossing of the phase back EMF
can be measured, we will know when to commutate the
current. As mentioned before, at one time instant, since only
two phases are conducting current, the third winding is open.
This opens a window to detect the back EMF in the floating
winding
D.Problem Statement
The phase back electromotive force (emf) zero-crossing points
(zcps) detection method [7] – [10]] is widely used in the
position sensor less control. The zcps detection of phase
voltages is accomplished using the motor neutral voltage in
[7].the virtual neutral point is adopted in [8] for the motor in
which the neutral point voltage cannot be easily achieved.
Nonlinear behavior should not possible conventional BACK
EMF technique. The proposed speed regulator address the
above mentioned problems
II.PROPOSED SENSORLESS T-S FUZZY BLDC SPEED
REGULATOR
A. Back-Emf Integration Method
In this technique, the commutation instant is determined by
integration of the silent phase’s back-EMF (that is the
unexcited phase’s back-EMF). The main characteristic is that
the integrated area of the back-EMFs shown in Figure 11 is
approximately the same at all speeds. The integration starts
when the silent phase’s back-EMF crosses zero. When the
integrated value reaches a pre-defined threshold value, which
corresponds to a commutation point, the phase current is
commutated. If flux weakening operation is required, current
advance can be achieved by changing the threshold voltage.
The integration approach is less sensitive to switching noise
and automatically adjusts for speed changes, but low speed
operation is poor due to the error accumulation and offset
voltage problems from the integration [9]. As the back-EMF is
assumed to vary linearly from positive to negative (trapezoidal
back-EMF assumed), and this linear slope is assumed speed-
insensitive, the threshold voltage is kept constant throughout
the speed range.
Fig-1: Back EMF integration in single ZCP
Once the integrated value reaches the threshold voltage, a reset
signal is asserted to zero the integrator output. To prevent the
integrator from starting to integrate again, the reset signal is
kept on long enough to insure that the integrator does not start
until the residual current in the open phase has passed a
zerocrossing.
Fig-2: Proposed T_S Fuzzy BLDC regulator model
The use of discrete current sensors for each motor phase will
provide complete current feedback, but the cost associated
with individual current sensors (e.g., current transformers or
Hall-effect sensors) is often prohibitive. An appealing
alternative is the use of current sensors which are integrated
into the power switches, such as power MOSFET’S and
IGBT’s, which are available from several device
manufacturers with ratings up to several hundreds of volts and
several tens of amps. However, embedded current sensors
impose their own constraints; for example, the current sensing
terminal is not electrically isolated from the associated power
device. Also, the availability of new power integrated circuits
makes it possible to take more complete advantage of these
sensors for the combined purposes of current regulation and
overcurrent protection. Finally, the back-EMF integration
approach provides significantly improved performance
compared to the zero-crossing algorithm explained before.
Instead of using the zero-crossing point of the back-EMF
waveform to trigger a timer, the rectified back-EMF waveform
is fed to an integrator, whose output is compared to pre-set
threshold. The adoption of an integrator provides dual
advantages of reduced switching noise sensitivity and
automatic adjustment of the inverter switching instants
according to changes in rotor speed

3. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.45-49
ISSN: 2278-2400
47
B. Sliding Mode Observer
For controlling BLDC motor, it is necessary to know an
absolute position of the rotor, so an absolute encoder or
resolver can be used for sensing the rotor position. But, these
position sensors are expensive and require a special
arrangement for mounting. Also, the state equation of BLDC
motor is nonlinear, so it is difficult for the linear control theory
to be applied and the stability of position and velocity
estimation have not been clarified. To improve the mechanical
robustness and to reduce the cost of the drive system, several
estimation techniques eliminating the encoder or resolver can
be applied [7]. Some relevant methods have been developed
using the sliding-mode observer. The SMO is widely studied
in the field of a motion control, and it can be applied to
nonlinear systems, such as BLDC motors [5]. This technique
applied to control systems encounters restrictions in practice,
due to the high voltage values of the power supply needed and
severe stress given to the static power converters. On the other
hand, the sliding mode has been shown very efficient in the
state estimation due to its salient features, i.e., robustness to
parameter variations and disturbances including the
measurement noise. The use of sliding mode in state observer
does not present physical restrictions relative to the
convergence condition (the estimation error moves toward
zero) and does not subject the system to undesirable chattering
.These problems can be alleviated using a binary observer with
continuous inertial Coordinate-Operator Feedback
C.T-S Fuzzy ModelTakagi and Sugeno proposed an elegant
modeling method, which is often referred to as the T-S fuzzy
model, to represent or approximate a nonlinear dynamical
system. In each fuzzy rule, a linear model is given to describe
the nonlinear system locally. The overall system is described
by fuzzily “blending” those local models. A typical IF-THEN
rule of T-S fuzzy models is written as
Rule i: IF
i
p
p
i
M
t
z
M
t
z is
)
(
...
and
is
)
( 1
1
THEN
( ) ( ) ( )
( ) ( )
i i
i
t t t
t t
 

x A x B u
y C x , for
r
i ,
,
2
,
1 
 (1)
where 1( ) ( )
p
z t z t are the premise variables, and
i
j
M is
the corresponding fuzzy set for j= 1, …, p,
 T
n t
x
t
x
t
x
t )
(
,
),
(
),
(
)
( 2
1 

x is the state vector,
( ) m
t 
u is the input vector,
n
n
i



A ,
m
n
i



B ,
and
q n
i


C are the system matrixes for Rule i,
( ) q
t 
y is the output and r is the rule number. In
traditional fuzzy control approaches, 1( ) ( )
p
z t z t are the
state variables. In our study, the static output is used as the
input variable. Thus, in our implementation, p=q
and 1 1
( ) ( ), , ( ) ( )
q q
z t y t z t y t
  . In other words, if only
one output is considered, there is only one variable in the
premise part. It can be expected that the number of fuzzy rules
is significantly reduced.
III. SIMULATION RESULT
The Proposed sensor less BLDC motor system with a buck
converter is established to run the simulation in
MATLAB/Simulink.
Fig-3: Proposed Sensor less T-S Fuzzy BLDC Speed
Regulator
Error (e) and change in error (ce) are the inputs for the fuzzy
controller whereas the output of the controller is change in
duty cycle (Δdc). The error is defined as the difference
between the ref speed and actual speed, the change in error is
defined as the difference between the present error and
previous error and the output, the Change in duty cycle which
could be either positive or negative and added with the
existing duty-cycle to determine the new duty-cycle. Fuzzy
logic uses linguistic variables instead of numerical variables.
The process of converting a numerical variable in to a
linguistic variable is called fuzzification. Fuzzy logic linguistic
terms are most often expressed in the form of logical
implications, such as If-Then rules. These rules define a range
of values known as fuzzy membership functions [7]. Fuzzy
membership functions may be in the form of a triangle, a
trapezoid or a bell.
Fig-4: Basic structure of FLC with BLDC motor.
Fig-5 Membership functions of fuzzy controller.
Fig. 5 illustrates the membership function of fuzzy logic
controller that used the fuzzification of two input values and
defuzzification output of the controller. There are seven
clusters in the membership functions, with seven linguistic
variables defined as Negative Big (NB), Negative Medium
(NM), Negative Small (NS), Zero (Z), Positive Small (PS),

4. Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.45-49
ISSN: 2278-2400
48
Positive Medium (PM), and Positive Big (PB). Fig. 7 shows
the MATLAB simulation diagram of fuzzy logic controller. A
sliding mode rule-base, used in the fuzzy logic controller is
given in Table I. The fuzzy inference operation is implemented
by using the 49 rules. Fig. 8 Simulation diagram of proposed
sensor less speed control of BLDC.Fig. 6 illustrates the
membership function of fuzzy logic controller that used the
fuzzification of two input values and defuzzification output of
the controller. There are seven clusters in the membership
functions, with seven linguistic variables defined as Negative
Big (NB), Negative Medium (NM), Negative Small (NS),
Zero (Z), Positive Small (PS), Positive Medium (PM), and
Positive Big (PB). Fig. 7 shows the MATLAB simulation
diagram of fuzzy logic controller.
Fig -6: Fuzzy Rule Viewer of BLDC motor
Fig-7 Surface Viewer of T-S fuzzy logic
Fig-8: Back EMF of sensor less BLDC motor
Fig-9: Torque characteristics of sensor less BLDC motor
Fig-10: Speed response of sensor less drive technique using T–
S Fuzzy controller.
IV. CONCLUSIONS
A fuzzy logic controller (FLC) has been employed for the
speed control of PMBLDC motor drive and analysis of results
of the performance of a fuzzy controller is presented. The
modeling and simulation of the complete drive system is
described in this paper. Effectiveness of the model is
established by performance prediction over a wide range of
operating conditions. The simulation of Fuzzy Logic
controller, using MATLAB to control the speed of flexible
BLDC Motor, proves that the desired speed is attained with a
shorter response time, when compared with conventional
controllers. Sensor less Sliding-Mode BLDC MOTOR Speed
regulator using Class of Uncertain Takagi-Sugeno Nonlinear
Systems controller is a superior controller and is capable of
controlling the motor drive over wide speed range in that time
of uncertainty
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