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Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513
of Emerging
in Engineering
and Applied
Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016)
Journals, 2011
(ISSN: 2141-7016)
Sensorless Control of Induction Motor Drive Using
SVPWM - MRAS Speed Observer
K. Suman and V. Aditya
C.V.S.R College of Engineering, Hyderabad, India
Corresponding Author: K. Suman
This paper proposes a novel Space Vector Pulse width modulation (SVPWM) for sensor less control of
induction motor using model reference adaptive system (MRAS). The steady state ripples in the torque are
present in the conventionally used MRAS sensor less control of induction motor which utilizes normally used
voltage source inverters. Also performance of the steady state speed is not as perfect as required having
disturbances in steady state region. Hence to improve the performance of MRAS based speed observer a novel
method of SVPWM based on reference voltage vector that utilizes the control variables as stator flux
components is proposed. By using the proposed SVPWM control of induction motor the speed disturbances
which are obtained are minimized and the speed performance is improved. Also the ripples present in the
electromagnetic torque are reduced. This is proved by the simulation results for conventional MRAS speed
observer and proposed SVPWM based MRAS speed observer.
Keywords: sensor less control, model reference adaptive system, reference voltage vector, SVPWM
Induction motors have been widely used in high Simulations. The studies include the level of
performance ac drives, requiring information. difficulties in tuning the adaptive Gain constants and
Introducing a shaft speed sensor decreases system the tracking performances of both speed estimators. To
reliability, and different solutions for sensor less ac obtain accurate estimation of the speed, a simple ondrives have been proposed. The MRAS speed line identification approach has been incorporated.
estimators are the most attractive approaches (Hori and Based on the theory of MRAS, simultaneous estimation
Uchida, 1991) due to their design simplicity. The of rotor speed has been described in this paper.
MRAS is based on principle, in which the outputs of Comparing to other adaptation techniques, this method
two models –one independent of the rotor speed is simple and needs a low computation power and has a
(reference model) and the other dependent (adjustable high speed adaptation even at zero speeds. This method
model)- are used to form an error vector. The error because eliminates the produced error in the speed
vector is driven to zero by an adaptation mechanism adaptation, is more stable and robust. Computer
which yields the estimated rotor speed. Depending on simulations results are presented to show its
the choice of output quantities that form the error effectiveness.
vector, several MRAS structures are possible. The most
common MRAS structure is that based on the rotor flux In SVPWM methods, the voltage reference is provided
error vector (Holtz 1996) which provides the advantage using a revolving reference vector. In this case
of producing rotor flux angle estimate for the field- magnitude and frequency of the fundamental
orientation scheme. Other MRAS structures have also component in the line side are controlled by the
been proposed recently that use the back EMF and the magnitude and frequency, respectively, of the reference
reactive power as the error vectors estimators. It is the voltage vector. Space vector modulation utilizes dc bus
intention of this paper, therefore, to present a direct voltage more efficiently and generates less harmonic
comparison between two different MRAS approaches: distortion in a three phase voltage source inverter.
the rotor flux based and the back EMF based MRASS
(Lassaâd and Ben Hamed, 2007). The rotor flux based Space Vector Pulse Width Modulation
MRAS approach studied in this paper basically follows Space Vector Modulation (SVM) was originally
that of, while the back EMF based approach is the developed as vector approach to Pulse Width
modified form of the one developed in. To allow for a Modulation (PWM) for three phase inverters. It is a
fair comparison, no on-line parameter methods will be more sophisticated technique for generating sine
incorporated and same current controllers and a PWM wave that provides a higher voltage to the motor with
generation technique will be used in both approaches. lower total harmonic distortion. The main aim of any
In order to compare the performances of the two modulation technique is to obtain variable output
estimators. Several performance measures are evaluated having a maximum fundamental component with
in computer
minimum harmonics. Space Vector PWM (SVPWM)
Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016)
method is an advanced; computation intensive PWM
method and possibly the best techniques for variable
frequency drive application (Erfidan et al., 2004).
equivalent to an orthogonal projection of [a b c] onto
the two-dimensional perpendicular to the vector [1 1
1] (the equivalent d-q plane) in a three-dimensional
coordinate system. As a result, six non-zero vectors
and two zero vectors are possible. Six non-zero
vectors (V -V ) shape the axes of a hexagonal as
The space vector concept, which is derived from the
rotating field of induction motor, is used for
modulating the inverter output voltage. In this
modulation technique the three phase quantities can
be transformed to their equivalent two-phase quantity
either in synchronously rotating frame (or) stationary
frame. From these two-phase components, the
reference vector magnitude can be found and used for
modulating the inverter output. The process of
obtaining the rotating space vector is explained in the
following section, considering the stationary
reference frame. Considering the stationary reference
frame let the three-phase sinusoidal voltage
component be,
V = V Sinωt
V = V Sin(ωt-4π/3)
V ) and are at the origin and apply zero voltage to the
load. The eight vectors are called the basic space
vectors and are denoted by (V , V , V , V , V , V ,
V , V ). The same transformation can be applied to
the desired output voltage to get the desired reference
voltage vector in the d-q plane. The objective of
SVPWM technique is to approximate the reference
voltage vector V using the eight switching patterns.
V = V Sin(ωt-2π/3)
depicted in Figure-2, and supplies power to the load.
The angle between any adjacent two non-zero vectors
is 60 degrees. Meanwhile, two zero vectors (V and
One simple method of approximation is to generate
the average output of the inverter in a small period T
to be the same as that of V in the same period
When this three-phase voltage is applied to the AC
machine it produces a rotating flux in the air gap of
the AC machine. This rotating resultant flux can be
represented as single rotating voltage vector. The
magnitude and angle of the rotating vector can be
found by means of Clark’s Transformation as
explained below in the stationary reference frame. To
implement the space vector PWM, the voltage the
stationary dq reference frame that consists of the
horizontal (d) and vertical (q) axes as depicted in
Figure-1. From Figure-1, the relation between these
two reference frames is below
Figure-2. Basic switching, vectors and sectors
Switching States
For 180° mode of operation, there exist six switching
states and additionally two more states, which make
all three switches of either upper arms or lower arms
ON. To code these eight states in binary (one-zero
representation), it is required to have three bits (2 =
8). And also, as always upper and lower switches are
commutated in complementary fashion, it is enough
to represent the status of either upper or lower arm
switches. In the following discussion, status of the
upper bridge switches will be represented and the
lower switches will it’s complementary. Let "1"
denote the switch is ON and "0" denote the switch in
OFF. Table-1 gives the details of different phase and
line voltages for the eight states (Rathnakumar et al,.
Figure-1. The relationship of abc reference fram and
stationary dq reference frame.
and f denotes either a voltage or a current variable.
As described in Figure-1. This transformation is
Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016)
MRAS Based On Rotor Flux-Linkage Estimation
The proposed MRAS is using state observer model
with current error feedback and rotor current model
as two models for flux estimation. Figure 5 shows the
block diagram of the proposed MRAS.
The reference model is given by:
The adjustable model is given by:
Table-1. Switching patterns and output vectors.
Model Reference Adaptive System
The Model Reference Adaptive Systems (MRAS)
approach uses two models. The model that does not
Involve the quantity to be estimated (the rotor speed
re in our case) is considered as the reference model.
The model that has the quantity to be estimated
involved is considered as the adaptive model (or
adjustable model). The output of the adaptive model
is compared with that of the reference model, and the
difference is used to drive a suitable adaptive
mechanism whose output is the quantity to be
estimated (rotor speed in our case). The adaptive
mechanism should be designed to assure the stability
of the control system. Figure 3 illustrates the basic
structure of MRAS. Different approaches have been
developed using MRAS, such as rotor-flux-linkage
estimation-based MRAS, back-EMF based MRAS
(reactive-power-based MRAS) (Lassaâd and Ben
Hamed, 2007). The Overall system of the proposed
sensor less control algorithm is shown in Figure 2.
estimated values of rotor fluxes in state
observer model
estimated values of rotor fluxes in rotor
current model. Rotor speed is obtained from the
adaptation mechanism as follows(Marwali and Ali
Keyhani, 1997)
The presence of the pure integrators brings the
problems of initial conditions and drift. In (Marwali
and Ali Keyhani, 1997), To reduce the effect of the
derivative terms, a similar approach as that used to
eliminate the pure integration problem a low pass
filter was used to replace the pure integrator, but the
performance in the low speed range is not satisfying,
for reasons which will be explained later.
Figure 3. Block diagram of the proposed MRAS
Figure 5. MRAS based on rotor Flux-linkage
MRAS Based on Back-EMF Estimation
This paper proposes a novel sensor less control
algorithm based on the MRAS for the speed sensor
less control of a induction motor. The proposed
MRAS is using the state observer model of and the
magnet flux model of and as two models for the
back-EMF estimation. The rotor speed is generated
from the adaptation mechanism using the error
Figure 4. Configuration of overall system
Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016)
between the estimated quantities obtained by the two
models as follows in Figure.6 (Toufouti, Meziane,
Benalla, 2006).
The reference model is given by:
To validate the performances of the proposed
controller, we provide a series of simulations and a
comparative study between the performances of the
proposed control strategy. A 1.5kW induction motor
with controller is simulated using the nonlinear
The results with the above scheme in steady state
operation are shown below. It shows the behavior of
the Electromagnetic torque, Stator current, THD
response of stator current at steady state
The adjustable model is given by:
The adaptive mechanism is given by :
Where Ki, and Kp, are the gain constants, i, and p are
the estimated values of back-EMF in the state
observer model. Figure 7 shows the block diagram of
the proposed MRAS algorithm has a robust
performance through combining the state observer
model and the magnet flux model.
a)Electromagnetic torque
Figure 6: The MRAS speed observer
This scheme does not have pure integrators in the
Reference model.
b) stator current
Figure 7: MRAS based on back –EMF
c) THD response of stator current
Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS) 2 (3): 509-513 (ISSN: 2141-7016)
It can be observed from the results that the value of
Total harmonic Distortion (THD) is just 24.58%
which is very low. So by using SVPWM–MRAS
speed Observer technique Total Harmonic Distortion
is also minimized
T. Erfidan, S. Urugun, Y. Karabag and B. Cakir.
2004. New Software implementation of the Space
Conference. pp.1113-1115.
Y. Hori C. Ta, T. Uchida (1991): MRAS-based speed
Sensor less control for induction motor drives using
instantaneous reactive power. IECON, Pp. 1417-1422
This work has dealt with the sensor less control of
induction motor with svpwm. Its principles and basic
concepts have been introduced and thoroughly
explained. This paper is focused on the analysis of
SVPWM-MRAS speed control schemes. The
simulation shows a SVPWM-MRAS has better
Zhou K., Wang D., Relationship Between SpaceVector Modulation and Three Phase Carrier-Based
Transactions on Industrial Electronics, Vol. 49, No.
1, February 2002, page 186-196
D. Rathnakumar, J. Lakshmana Perumal and T.
Srinivasan. 2005. A New software implementation of
space vector PWM. Proceedings of IEEE Southeast
conference. pp.131-136.
D.W. Jin Y.A. Kwon. (1999): A novel MRAS based
speed sensorless control of induction motor. IECON,
J. Holtz, (1996): “Methods for speed sensorless
control of ac drives,” in Sensorless Control of AC
Motors, K. Rajashekara, Ed. Piscataway, NJ: IEEE
Mohammad N. Marwali and Ali Keyhani (1997):
‘’A Comparative Study of Rotor Flux Based MRAS
and Back EMF Based MRAS Speed Estimators for
Speed Sensorless Vector Control of Induction
Machines’’ IEEE Industry Applications Society
Annual Meeting New Orleans, Louisiana, October 59
R. Blasco-Gimenez., G.M. Asher, M. Sumner, and K.
J. Bradley (1996): "Dynamic Performance
Limitations for MRAS based sensorless induction
motor drives. Part 2: Online parameter tuning and
dynamic performance studies", IEE Proc. Elect.
Power Appl, Vol. 143, (2), pp. 123-134.
R.Toufouti S.Meziane ,H. Benalla,(2006): “Direct
Torque Control for Induction Motor Using Fuzzy
Logic” ICGST Trans. on ACSE, Vol.6, Issue 2, pp.
S.Lassaâd and M.Ben Hamed (2007): “An MRAS based full Order Luenberger Observer for Sensorless
DRFOC of Induction Motors” ICGST-ACSE Journal,
Volume 7, Issue 1.
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