1. International Journal of Electronics and Communication Engineering & Technology (IJECET),
ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 4, Issue 3, May – June (2013), © IAEME
282
SIMULATION OF IGBT BASED SPEED CONTROL SYSTEM FOR
INDUCTION MOTOR USING FUZZY LOGIC
Abhijit D. Ghorapade1
, Snehal S. Mule2
1
PG Student of Sinhgad College of Engineering, Pune University of pune, India.
2
PG Student of DKTE’S TEI, Ichalakaranji, Shivaji University Kolhapur, India.
ABSTRACT
This research paper presents modeling and simulation of fuzzy logic based speed
control system for induction motor using PWM Insulated Gate Bipolar Transistor (IGBT)
inverter as a switching device. The model is implemented on computer simulation using
MATLAB/Simulink software with different Block Sets. Fuzzy Logic Controller is developed
using fuzzy logic toolbox. The control design includes some rules. These rules show a good
relationship between two inputs and an output, all of which are nothing but normalized
voltages. The inputs to the controller are speed error, derivative of speed error means Change
of error, and the output is Change of control. The errors are evaluated according to the rules
in accordance to the defined membership functions. The membership functions and the rules
have been defined using the FIS editor. The obtained Simulation results of
MATLAB/Simulink in relation with electromagnetic torque and speed are given for
effectiveness of the study.
Key words: Electromagnetic torque, Fuzzy Logic Controller, IGBT, PWM, membership
functions.
1. INTRODUCTION
The use of Induction Motors (IM) has increased significantly from the day of its
creation. They are mainly used in various industrial processes, robotics, house appliances and
other similar applications. The reason for its amassed popularity is its robust construction,
design simplicity and cost. [1]-[3] Induction motors are more reliable than DC motors.
Now days speed control of induction motor is very important because they are
operated at different speed. Depending on application its speed will be selected. But the
control of IM is complicated due to its nonlinear behavior and its parameters changes with
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operating conditions. [4, 5] Hence in the last few years it has been studied, and various
methods for the same have been developed. The Volts/Hertz control scheme is popular
because it offers a wide range for speed control with good running performance.[5, 6]
Pulse Width Modulation (PWM) with flexible speed drive systems are mostly used in
industrial applications for controlling where greater performance is desired. A number of
Pulse width modulation systems are used for variable voltage and frequency supply
requirement. Today there is a huge development in power electronics and semiconductor
technology. This lead improvement in power electronic systems. Therefore different power
circuits like inverters have become popular. [7]-[9] Variable voltage and frequency supply is
achieved from inverter. Recently IGBT PWM inverters are widely used inverters, because of
their improvement in harmonic quality at the output as compared to the other inverters. Now
GTO devices are replaced by IGBTs because of their fast evolution in voltage and current
ratings and higher switching frequency. [8, 9]
In the last few years, Fuzzy logic is popular in motor control applications due to its
non-linearties handling ability and independence of the plant modeling. The fuzzy logic
controller (FLC) operates in a knowledge-based way. Its control performance is less affected
by system parameter variations. [9, 10] A fuzzy logic system uses linguistic if then rules
based on systems qualitative aspects and expert knowledge.
This paper therefore proposes a simple alternative method for controlling speed of
induction motor with PWM IGBT inverter, it uses only fewer values, rules, and decisions
making. These rules are Linguistic, not numerical and cover great complexity.
This paper is organized as follows. In section 2 the aspects of IGBT PWM Inverter,
V/f control strategy and its operational principle is presented. In section 3 the block diagram
of system is described in detail. In section 4 design of FLC using membership function and
FIS system is explained. In section 5 Simulink model of fuzzy logic controllerand simulation
results are discussed. In section 6 the main conclusions are outlined.
2. PULSE WIDTH MODULATION IN INVERTERS
2.1 IGBT PWM Inverter
For speed control of induction motor output voltage of an inverter must be adjusted by
exercising a control within the inverter. Therefore this paper uses most effective method a
pulse-width modulation control in inverter. In this method, a fixed dc input voltage is given
to the inverter and a controlled ac output voltage is obtained by adjusting the on and off
periods of the inverter components. This is the simplest method of controlling the output
voltage. This method is termed as Pulse-Width Modulation (PWM) Control. [11]-[14]
Generally power bipolar transistors and MOSFET’S are used for inverters to driving
ac motors. An alternative inverter, insulated-gate-bipolar transistors (IGBT’s) is developed
recently. [14] IGBT’s offers low ON resistance and need small gate drive power. A gate-
drive circuit provides fast turn-on and turn-off, and adequate protection against
overload/shoot through faults presented in IGBT. Snubbers are required to limit the stresses
on the IGBTs at turn-on and turn-off. [14, 15]
The IGBT Inverter combines the properties of both MOSFET'S and bipolar-
transistors. It gives low saturation voltage and its voltage driven input. It uses very little drive
power. It has two control terminals gate and emitter. The IGBT will turn-on when a voltage
VGE greater than gate-emitter threshold voltage VGEth is applied between the gate and emitter.
To turn-off the IGBT a resistance RGE is connected between gate and emitter, which provides
a discharge path for the gate-to emitter capacitance. The IGBT shows a large current fall time

3. International Journal of Electronics and Communication Engineering & Technology (IJECET),
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at turn-off. The fall time consists of two different intervals, one of which is constant and the
other is depends on RGE. For smooth switching and to reduce the switching losses IGBT uses
some additional networks. [15, 16]
Fig1:
The configuration for IGBT PWM In
for the auxiliary winding is higher than that for main winding. The sinusoidal PWM signal,
which is responsible for driving the IGBT inverter, was generated by Discrete PWM
Generator.
2.2 V/f Control Strategy of Induction Motor
The induction motor’s speed
draws rated current and provides the rated torque at the base speed.[17]
Induction motor is given by following equation.
Stator Voltage (V)
φ∝
In V/F control, variable frequency signals generated by the PWM control of an
inverter and this signal is given to the Motor. In this the V/f ratio is maintained constant to
get constant torque for the total operating range. As simply magnitudes of the input variables
frequency and voltage are controlled, this is known as “scalar control”. For this type of
controlling little knowledge of the motor is essential. In this type of control the torque
developed is load dependent as it is not controlled
parameter values used in proposed model.
Table 1:
Stator resistance
Rotor resistance
Number of pole pairs
Stator inductance
Rotor inductance
Mutual inductance
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off. The fall time consists of two different intervals, one of which is constant and the
. For smooth switching and to reduce the switching losses IGBT uses
some additional networks. [15, 16]
Fig1: Power circuit Topology for IM
The configuration for IGBT PWM Inverter is shown in Fig.1. Normally, the voltage
winding is higher than that for main winding. The sinusoidal PWM signal,
which is responsible for driving the IGBT inverter, was generated by Discrete PWM
V/f Control Strategy of Induction Motor
The induction motor’s speed- toque characteristic states that the induction motor
draws rated current and provides the rated torque at the base speed.[17]-[19] The relation of
Induction motor is given by following equation.
Stator Voltage (V) ∝ Stator Flux (φ) x Angular Velocity (ω)
V ∝ φ x 2πf
V/f (1)
In V/F control, variable frequency signals generated by the PWM control of an
inverter and this signal is given to the Motor. In this the V/f ratio is maintained constant to
total operating range. As simply magnitudes of the input variables
frequency and voltage are controlled, this is known as “scalar control”. For this type of
controlling little knowledge of the motor is essential. In this type of control the torque
ped is load dependent as it is not controlled directly. [18]-[20] The Table.1
parameter values used in proposed model.
able 1: Induction Motor Parameters
Stator resistance 5
Rotor resistance 5
Number of pole pairs 2
Stator inductance 0.0073H
Rotor inductance 0.016H
Mutual inductance 0.2775H
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off. The fall time consists of two different intervals, one of which is constant and the
. For smooth switching and to reduce the switching losses IGBT uses
1. Normally, the voltage
winding is higher than that for main winding. The sinusoidal PWM signal,
which is responsible for driving the IGBT inverter, was generated by Discrete PWM
characteristic states that the induction motor
[19] The relation of
In V/F control, variable frequency signals generated by the PWM control of an
inverter and this signal is given to the Motor. In this the V/f ratio is maintained constant to
total operating range. As simply magnitudes of the input variables
frequency and voltage are controlled, this is known as “scalar control”. For this type of
controlling little knowledge of the motor is essential. In this type of control the torque
[20] The Table.1 show the

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3. BLOCK DIAGRAM OF SYSTEM
The Block diagram of proposed system used for speed control of an induction motor
is shown in Fig. 2. In the block diagram output of PWM inverter is the reference signal. The
current speed of the motor (ωm) is compared with the reference speed (ωr), and provides
speed error (e). This mechanism is called the feedback mechanism. Change-of-error ( e), that
is, the derivative of speed error (e) is computed and both (e) and ( e) are fed to the fuzzifier
for fuzzification. The inference system then processes these two fuzzy inputs using the fuzzy
control rules and the database, which are defined by the programmer based on the chosen
membership function. The obtained fuzzy output is defuzzified by the defuzzifier to give a
crisp value, i.e. Change of control (ωsl), and it is applied as a input to the PWM Inverter. The
PWM Inverter uses this input to generate a voltage whose frequency and amplitude can be
varied by the Fuzzy Logic Controller itself via the above mentioned process. The voltage is
fed to the auxiliary winding of induction motor which then runs with a speed which tends to
follow the desired speed.
Fig 2: Block Diagram of Fuzzy Logic Control System
4. DESIGN OF FUZZY LOGIC CONTROLLER
4.1 Designing of Membership functions
The design of a Fuzzy Logic Controller (FLC) uses the membership functions. The
membership functions are selected such that they cover the whole universe of discourse.The
membership functions must be overlap on each other to avoid any kind of discontinuity with
respect to the minor changes in the inputs. To accomplish greater control, the membership
functions near the zero region must be narrow, and away from the zero region membership
functionsare wider which results the faster response to the system. Therefore, the membership
functions for input and output variables are adjusted accordingly. After the selection of
proper membership functions a rule base is created. It consists of a number of fuzzy If-Then
rules according to the behavior of the system. These rules are similar to the human thought
process, means here artificial intelligence is provided to the system.

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Fig3: Membership function for input variables speed error (e)
Fig 4: Membership function for Change-of-error ( e)
The seven linguistic terms are used for two inputs and one output of fuzzy logic
controller. these are as follows “NB” is “Negative Big”, “NM” is “Negative Medium”, “NS”
is “Negative Small”, “ZE” is “Zero”, “PS” is “Positive Small”, “PM” is “Positive Medium”
and “PB” is “Positive Big”. Membership functions for both input variables speed error (e)
and Change-of-error ( e) are shown in Fig.3 and Fig.4.
Total 49 rules are written in the program according to the syntax provided by MATLAB. If-
Then Rules used for the design of the Fuzzy Logic Controller are represented as follow:
IF (Error is NB) AND (Change in Error is NS)THEN (Change of control is NB)
IF (Error is NB) AND (Change in Error is ZE)THEN (Change of control is NM)
The table representing the rule base is as shown in Table 2.

6. International Journal of Electronics and Communication Engineering & Technology (IJECET),
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Table 2: Fuzzy rule table for output (ωsl)
4.2. FIS System
Now day’s Fuzzy Inference System (FIS) is magnificently applied for automatic
control modeling. Mamdani’s fuzzy inference method is the most commonly used
methodology.[21, 22] There are five parts of the fuzzy inference process first is fuzzification
of the input variables, second is application of the AND operator to the antecedent, Third is
implication from the antecedent to the consequent, Fourth is aggregation of the consequents
and fifth is defuzzification.
5. SIMULATION MODEL OF SYSTEM AND SIMULATION RESULTS
Simulation diagram of proposed system is shown in Fig.5. The model is implemented
using MATLAB/Simulink software with the Sim Power System, Simulink and fuzzy logic
Block Sets.
Fig 5:Simulation diagram of proposed FLC system

7. International Journal of Electronics and Communication Engineering & Technology (IJECET),
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Fig 6: Main Winding current of FLC Controller
Fig 7:
Fig 8:Torque response of FLC Controller
Fig 9: The Surface Viewer of proposed system
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Main Winding current of FLC Controller
Speed response of FLC Controller
Torque response of FLC Controller
The Surface Viewer of proposed system
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The plots for speed, current, and torque for Fuzzy Logic Controller were observed and
shown in Fig.6, 7 and 8. It can be seen from the above figures that while using the Fuzzy
Logic Controller the settling time is less. FLC approaches the new reference speed faster.
From the current plot, the same can be achieved. Fig.6 shows in the current plot the current is
sinusoidal. But there is a distortion in the envelope before the machine attains steady state.
Because at the time of starting the machine goes through the unstable region.Fig.7 shows the
Fuzzy Logic Controller attains a steady state speed in small period, and, it is very much close
to the reference speed.The torque plot is shown in Fig. 8. It illustrate that the Fuzzy Logic
Controller gives oscillations during starting period and after that it gives smooth
response.This is because motor provides a desirable response after some time as the
controller first has to study and then adjust according to the data provided by the user. The
Surface Viewer of proposed system is shown in Fig.9.
6. CONCLUSION
The performance of proposed fuzzy control system is satisfactory in relation to load
torque variations when it achieves the reference speed. By using FLC we can avoid the
numerical calculation involved in higher order systems. Fuzzy logic provides artificial
intelligence to the controllers. This is not offered by the conventional controllers. After the
simulation of the of the block diagram in MATLAB/SIMULINK®, it was found that the
fuzzy logic controller used in the simulation worked quite effectively.
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AUTHORS DETAIL
Abhijit D. Ghorapade was born in Sangli, Maharashtra, in 1986.
He graduated in Electronics and Telecommunication Engineering at
the Shivaji University Kolhapur, Maharashtra, in 2007. He is a post
graduate student of SCOE Pune from the University of Pune
Maharashtra, His research interests are within the fields of Fuzzy
systems, embedded systems and control.
Snehal S. Mule was born in Sangli, Maharashtra, in 1988. She
graduated in Electronics and Telecommunication Engineering at the
Solapur University Solapur, Maharashtra, in 2010. She is a post
graduate student of DKTE’S TEI Ichalakaranji from the Shivaji
University Kolhapur, Maharashtra, Her research interests are within
the fields of intelligent systems, Power Electronics and control.