Performance of ANN Based Indirect Vector Control
Induction Motor Drive
Rani S & Victore George
E-mail : rany.ktm@gmail.com
Abstract – Induction motors are extensively used in
industrial and household appliances consume about 60%
of the total consumed electrical energy. The need for
energy conservation is increasing the requirements for
saving the electrical energy. It is therefore important to
optimize the efficiency of motor drive systems if significant
energy savings are to be obtained. This paper proposes a
new control scheme based on artificial neural networks to
obtain certain torque and speed operating point there exist
only one set of voltage amplitude and frequency that
operates the machine at optimum efficiency. In this paper
indirect field oriented (IFOC) control is used. This is one of
the most effective vector control of induction motor due to
the simplicity of designing and construction.
I.
methods of their speed control have either been
expensive or highly inefficient. Various methods have
been designed to achieve the best performance of the
motor and the approaches used can be classified into
three different types. The first, so called “loss model
controller”. The second type, named “search controller”.
II. FIELD ORIENTED CONTROL
The vector control of ac drives has been widely
used in high performance control system. Indirect field
oriented control (IFOC) is one of the most effective
vector control of induction motor due to the simplicity
of designing and construction. In this system, the flux
coordinates reference frame is locked to the rotor flux
vector rotating at the stator frequency as shown in Fig.2.
This results in a decoupling of the variables so that flux
and torque can be separately controlled by stator directaxis current and quadrature-axis current respectively. It
is also known as decoupling, orthogonal or trans-vector
control.
INTRODUCTION
The efficiency of Induction Motor when operate at
rated speed and load torque is high. Unfortunately for
variable load operation the application of the motor at
rated flux will cause the iron losses to increase
excessively; hence its efficiency will reduce
dramatically. In order to reduce the iron losses the flux
level should be set lower than rated flux, but this will
increase the copper loss. Therefore, to optimize the
efficiency of the induction motor drive system at partial
load, it is essential to obtain the flux level that
minimizes the total motor losses.
The FOC block used here gets the value of the flux
and voltage frequency ratio to give gating pulses to the
inverter which controls the voltage being fed to the
induction motor and hence the efficiency optimization is
obtained to achieve the task of energy saving. The
feedback is again send to the speed controller for the
optimization purpose. Point to another through the
adequate optimal trajectory and the other which is in
charge of keeping the motor at a maximum efficiency
operating point in the steady state for a given speed and
torque. The first part acts when a modification in the
reference speed is produced. Its function consists of
determining the necessary optimal trajectory, starting
from the present state of the motor and the aimed speed,
creating the adequate reference signals. Once the desired
speed is reached, the motor changes into steady state
with minimum stator current for the new load torque.
From this moment, the second algorithm acts.
In air conditioning system, the induction motors are
often used to drive a compressor at constant speed
operation. However, because the typical load profile of
this load is that the load torque varies with speed,
therefore implementation of a variable speed drive for it
is potential to increase energy saving. Because of their
easy implementation and low cost, indirect field
oriented induction machine drives are finding numerous
industrial applications. Most of the industrials motors
are used today are in fact induction motors. Induction
motors have been used in the past mainly in applications
requiring a constant speed because conventional
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International Journal on Advanced Computer Theory and Engineering (IJACTE)
The search for the maximum efficiency operating
point is based on and consists of an iterative algorithm,
which applies small increments to the reference signal
while reference signal is in charge of maintaining the
required torque and speed. A step followed by a
decrease in the consumed active electric power means
that it acts in the correct direction and must be repeated.
Otherwise, a successive step will be reversed. A special
algorithm that selects the most convenient state of the
three-phase inverter among the eight possible ones
achieves the closeness of the stator current space vector
to its reference signal in both transient and steady states.
In this way, the inverter, in spite of being a voltagesource one, acts as a current source that permits us to
ignore the voltage equations of the motor model
as mentioned.
Like the human brain the ANN can be trained to
solve the lost complex non-linear problems with
variable parameters. The ANN has been successfully
applied to identify and control the currents of an
induction machine. There have been several applications
of ANN in AC drive systems such as adaptive flux
control, current control, speed control, and field-oriented
control. It is expected that ANN as an artificial
intelligence tool will guide to new modern techniques in
power electronics and motion control systems. As
mentioned earlier speed and torque estimation two other
applications for the ANN in motion control.
Fig. 3 : ANN with separate hidden layers for speed and torque
estimation.
A unique property of a neural network is that it can
still perform its overall function even if some of the
neurons are not functioning. That is, they are very robust
to error or failure. Neuron is a fundamental processing
component of a neural network. Once the network is
structured, network is ready to train. For this initial
weights are chosen randomly. Here, neural network is
used for compute the appropriate set of voltage and
frequency to achieve the maximum efficiency for any
value of operating torque and motor speed. NN have the
two inputs, motor torque and speed and have two
outputs, optimum voltage and frequency.
Fig.1: Vector drive with ANN based feedback signal
processing.
The two layer feed forward structure of NN model
is used in this paper. It has two hidden layers; one is of
tan-sigmoid function. Since the optimum voltage and
frequency are highly non-linear function of motor speed
and load torque so we preferred the tan-sigmoidal
function and other is of pure linear function. The two
inputs of NN model is firstly feed to the layer of tansimoidal function and output of this layer is broadcast to
the layer of linear neurons.
Fig. 2 : Neural network system to calculate commanded
voltage and frequency.
III. ANN MODEL
Artificial Neural networks are relatively crude
electronics models based on the neural structure of the
human brain. The fundamental processing element of a
neural network is a neuron. A neural net is based on
layers of neurons. The outputs are the operating speed
and torque of the artificial neural networks is valuable
on several counts including their adaptability, nonlinearity, and generalization capabilities. The ANN is
the simulation of human brain nervous system and is
constructed of artificial neurons And their
interconnections.
Fig. 4: Neural network model.
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The back-propagation training algorithm is used for
this network. The training is automated with Mat lab
Simulation program developed for this purpose, which
train the network using the back propagation method. At
the end of training of network the value of weight and
bias are set. The torque and speed should vary with in a
practical limit corresponding the value of voltage and
frequency is calculated and saved in a data file in matrix
form.
machine model as shown. The controller makes two
stages of inverse- transformations, as shown, so that the
control currents ids* and iqs* corresponds to the machine
currents ids and iqs, respectively. In addition, the unit
vector assures correct alignment of ids current with flux
vector ѱr and iqs perpendicular to it. Note that the
transformation and inverse transformation including the
inverter ideally do not incorporate any dynamics, and
therefore, the responds to ids and iqs is instantaneous.
IV. VECTOR CONTROL
The vector control of ac drives has been widely
used in high performance control system. In the case of
induction machines, the control is usually performed in
the synchronously rotating reference frame. In vector or
field oriented control both the magnitude and phase
alignment of vector variables are controlled The stator
current of induction machine are separated into
flux- (Isd ) and torque producing (Isq) components by
utilizing
transformation to the d-q coordinate system,
whose direct axis (d) is aligned with the rotor flux space
vector. This means that the q-axis component of the
rotor flux space vector is always zero.
ѱrq =0
d ѱrq=0
dt
Fig. 6: Complete block diagram of vector control.
Fig.6 shows the basic structure of the vector control
of the AC induction motor. To perform vector control,
follow these steps:
Fig.5: Vector control implementation principle with machine
de-qe model.
In Fig.5 the machine model is represented in
synchronously rotating reference frame. Vector control
implementation can be explained with this fig. The
inverter is omitted from the figure, assuming that it has
unity current gain that is, it generates the currents ia, ib,
and ic as dictated by the corresponding command
currents ia* ib* and ic* from the controller.
A machine model with internal conversions is
shown in right. The machine terminal voltage ia, ib and ic
are converted into idss and iqss components by three phase
to two phase transformation. These are then converted to
synchronously rotating frame by the unit vector
component cosθe and sinθe before applying to the de-qe

Measure the motor quantities (phase voltages and
currents)

Transform them to the 2-phase system using a
Clarke transformation

Calculate the rotor flux space vector magnitude and
position angle

Transform stator currents to the d-q coordinate
system using a Park transformation

The stator current torque- (isq) and flux- (isd)
producing components are separately controlled

The output stator voltage space vector is calculated
using the decoupling block

An inverse Park transformation transforms the
stator voltage space vector back from the d-q
coordinate system to the 2-phase system fixed with
the stator
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International Journal on Advanced Computer Theory and Engineering (IJACTE)

Using the space vector modulation, the output 3phase voltage is generated
[5]
F.J.Nola, “Power factor Control System for AC
Induction Motor, “U.S.Patent 4 8, Oct 4,1977
Sѱrq = 0
[6]
Alexander Kusko, Donald Galler,”Control
Means For Minimization of Losses in AC and
DC Motor Drives”, IEEE Trans. Ind. Appl., vol.
LA-19, No. 4, July/August 1983
[7]
Iordanis Kioskeridis, Nikos Margaris, “Loss
Minimization in Induction Motor AdjustableSpeed Drives”, IEEE Trans. Ind. Electron, vol.
43, No. 1, February 1996
[8]
Iordanis Kioskeridis, Nikos Margaris, “Loss
Minimization
in
Scalar
Controlled
Induction Motor Drives with Search Controllers,
“IEEE Trans. Ind.Electron, vol. 11, No. 2, March
1996
[9]
K.M.Hasan, Li Zhang, B. Singh, “Neural
Network Control of Induction Motor Drives for
Energy Efficiency and High Dynamic
Performance”.
V. CONCLUSION
This paper has presented an ANN-based scheme for
induction motor drive leading to energy saving. The
proposed scheme uses information on speed and torque
of the motor to generate the appropriate voltage
amplitude and frequency that save the energy. An ANN
model has been configured and trained on a set of data
generated, based on the motor equivalent circuit, using a
Matlab computer program. The model uses two hidden
neuron layers. By using this technique we can obtain the
stable operating point. The corresponding values of
voltage and frequency at this point will operate the
machine at optimum efficiency.
VI. REFERENCES
[1]
Toshiaki Murata, Takeshi Tsuchiya and
Ikuo Takeda, “Vector Control for
Induction
Machine on the Application of Optimal Control
Theory,” IEEE Trans. on Industrial Electronics,
vol. 37, no. 4, pp. 283-290, 1990.
[10]
D.M.Breathauer, R.L.Doughty and R.J.Puckett,
“The Impact of Efficiency on the Economics of
New Motor Purchases, Motor Repair, and Motor
Replacement, “IEEE Trans. Ind. Appl., vol. 30,
Nov./Dec. 1994, pp. 1525-1537.
[2]
B.K.Bose, “Modern Power Electronics and AC
Drives”.
[11]
[3]
Performance of ANN based indirect vector
control induction motor drives. © 2007 JATIT,
www.jatit.org.
H.A.Al-Rashidi,
A.Gastli,
A.Al-Badi,
“Optimization of Variable Speed Induction Motor
Efficiency Using Artificial Neural Network”.
[12]
Howard Demuth, Mark Beal, “Neural Network
Toolbox For Use With Matlab”, User Guide, The
Math Work Inc. June 1992
[13]
Dan W. Patterson, “Introduction to Artificial
Intelligence and Expert System”.
[4]
M.G. Simoes and B. K. Bose, “ Neural network
based estimation of feedback Signals for vector
controlled induction
motor drive”, IEEE
Trans. Ind Apl., vol- 31 pp 620-629, May/June
1995.
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