 Open Access
 Total Downloads : 1176
 Authors : Gurmeet Singh, Gagan Singh
 Paper ID : IJERTV3IS080938
 Volume & Issue : Volume 03, Issue 08 (August 2014)
 Published (First Online): 28082014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Modeling and Simulation of Indirect Field Oriented Control of Three Phase Induction Motor using Fuzzy Logic Controller
Gurmeet Singh
Electrical Engineering Dept.
DIT University Dehradun, India
Gagan Singh
Electrical Engineering Dept.
DIT University Dehradun, India
AbstractIndirect field oriented induction motor drives are rapidly increasing in the industrial sector, but the performance of the drive decreases by the change in the motor parameters. This paper presents the modelling and simulation of indirect field oriented control of induction motor drives. This scheme is explained using field orientation control principle of induction motor drive fed through inverter. The fuzzy logic controller for speed control is used in the outer loop of the induction motor drive so that the machine will follow a reference model. To verify the design of the proposed controller, the simulation is carried out in the Matlab/Simulink platform. The simulation results are presented to demonstrate the effectiveness of the proposed fuzzy logic controller.
KeywordsInduction motor; Fuzzy logic control; indirect vector control.

INTRODUCTION
Over the years electric drives have emerged, and so their techniques to control their speed, torque and efficiency. It is advantageous to use AC motors as compared to DC motors as AC motors are cheaper and have simple mechanical structure. With the number of growing techniques such as vector control or field oriented control, the complicated control issues of the adjustable speed drive applications of the induction motor are solved. The controller design of the system plays a very important role in the performance of the system. However, it is difficult to develop a mathematical model for unknown load variations and parameter variations due to variations in temperature, saturation effects and the disturbances in the system
The vector control technique is used for high performance control of speed and torque. The performance of this method is the same as the performance is obtained from the separately excited dc motor. The vector control technique provides a decoupling control of the rotor flux and the torque producing component with a fast torque response.
In order to overcome the problem discussed above, a fuzzy logic controller is used for the control purpose. With a comparison with PI, PID and other adaptive control
systems, the FLC has various advantages as (1) there is no need of the exact system mathematical model (2) it handles the nonlinearity and the complexity of the system (3) it is based on the linguistic rules with an IFTHEN general structure, which uses the human experience and logic. However the fuzzy logic has some disadvantages also as it includes high computations, so there is a high computation burden

PRINCIPLE OF VECTOR CONTROL OR FIELD ORIENTED CONTROL
A renaissance in the high performance of induction motors has been brought by the demonstration that the induction motor can be controlled as a separately excited DC motor. The field oriented control has become very popular, as it guarantees high dynamic and static performance. FOC is based on the idea of decoupling torque and flux via nonlinear coordinate transformation and controlling these variables by acting on the direct and quadrature current vector components by means of unit vector (sine and cose). FOC has two methods: (a) direct or feedback vector control and (b) indirect or feedforward vector control. In direct FOC, unit vectors are generated by stator voltages and currents (Voltage model estimator) or by stator currents and rotor speed (Current model estimator). The drawbacks of this method are: (a) dependency on the rotor resistance value and (b) computation requirements and time delay due to the use of current control loops and axes transformation. As Field Orientated Control is simply based on projections the control structure handles instantaneous electrical quantities. This makes the control accurate in every working operation.
This paper presents a relatively simple fuzzy logic controller that is efficient in the speed tracking, rejecting the disturbances and the variation in the parameters without any need of the complex control technique which require mathematical models. This is achieved by carefully designing the linguistic rule base.

AXES TRANSFORMATION
1 1 ia
i
2 1 2 2
ib
(1)
i
3 0 3 3
The three coordinated are transformed in the in FOC
2 2
ic
system using the Clarke transformation, park
transformation and inverse park transformation.
id
cos
sin i
iq sin
cos i
(2)
Clarke transformation is that transforms from a three phase stationary coordinate system (a,b,c) to a twophase
i
cos sin id
stationary coordinate system (). Assuming that the axis
i sin
cos iq
(3)
a and axis are aligned in same direction. Clarke transformation equations are shown in equation (1). Park transformation is that transforms from a twophase stationary coordinate system () to a twophase rotating coordinate system (dq). The relationship of the two coordinate system is shown in Fig. 2 on the basis of the consideration that that the daxis is aligned with the rotor flux. The equation for park transformation is given in equation (2).

INDIRECT VECTOR CONTROL OF
INDUCTION MOTOR
PWM INVERTER
DQ ABC
i
* ds
i
*
r
* eFUZZY qs
CONTROL
LER
r
b
is
is
SLIP r
CALCULATOR
is
a
Fig 3. Block diagram of indirect vector control of induction motor drive
The indirect vector control of induction motor is the same as the direct vector control method but with a difference that the unit vectors are generated in an indirect
fashion using the measured speed
r and the slip
speed sl . The equations for the indirect vector control are
c as
Fig. 1. Stator currents space vectors and its components in ().
e edt (rsl)dtr sl
The rotor current equations are
(4)
ddr R r
dt Lr dr

Lm R rids slqr 0 Lr
(5)
dqr R r

Lm R i

0
(6)
q
dt Lr qr Lr r qs sl dr
is
For decoupling control qr 0 , so the total flux r directs
on the de axis.
Substituting this condition in equations (5) and (6), we get
isq
Lr d r
L i
(7)
is
r
isd
d
R r dt
r m ds
is
a
the slip frequency can be calculated as
sl LmR r iqs
r Lr
(8)
Fig. 2. Stator currents space vectors and its components in dq coordinate system.
Inverse park transformation is that which transforms a
For constant rotor flux r
equation (7) yields
r Lmids
and dqr 0 , substituting in
dt
(9)
twophase rotating coordinate system (dq) into a two phase stationary coordinate system (). The equation for
The electromechanical torque equation is given by
inverse park transformation is given in equation (3).
Te 3 P Lm /p>
2 2
Lr
riqs
(10)
The block diagram of the indirect vector control scheme is shown in Fig. 3.


FUZZY LOGIC CONTROLLER DESIGN FOR
INDUCTION MOTOR
From the knowledge generated over the fuzzy logic controllers, we have come to know that the fuzzy logic is the efficient way. In designing the controller, non linearities with the individuals with the individuals experience and expert knowledge about the process to be controlled. This approach enhances the performance, reliability and robustness of the system more than the conventional linear controllers.
The block diagram of fuzzy logic controller is shown in the Fig. 4. There are two inputs to the controller. The first input is the error e and the second input is the change in error e. The calculation of error and change in error is shown as
e t * t r t
(a)
(b)
s r s s
(11)
e ts e ts e ts1
r s
(12)
where,
* t is the reference speed of the rotor and
e ts1 is the value of the error at one sample before. The output of the fuzzy controller is the change in torque (T) which is then integrated to get the reference torque value, the equation is as
(c)
Fig. 6. Membership Functions; (a) for error; (b) for change in error; (c) output
T* ts T* ts1 T
(13)

Fuzzification
The fuzzy controller mainly consists of four blocks as fuzzification, inference mechanism, knowledge base and
In the process of fuzzification the crisp input variables
e tsand e ts are converted into fuzzy variables with a
defuzzification.
d
ce
dt
FUZZY CONTRO LLER
r e
*
r
U U
iqs iqs
INDUCTION MOTOR
degree of certainty given by the membership functions associated. The membership functions are of triangular shape, and have two inputs and one output. The linguistic labels of the proposed controller are NB, NM, NS, ZE, PS, PM, PB. All the inputs and output membership functions contains all these seven linguistics.

Knowledge base and inference mechanism
VECTOR CONTROL AND INVERTER
Fig. 4. Functional block diagram of Fuzzy Logic Controller
KNOWLEDGE BASE DATA BASE RULE BASE
Knowledge base includes defining the rules represented by IFTHEN general structures which governs the relationship between the input and output variables. The inference engine, which is based upon the input fuzzy sets, uses the IFTHEN rules in the knowledge base to make the decisions. tn this the mamdanis algorithm is used for
ce
e
* qs
DEFUZZIFIC ATION
INFERENCE MECHANISM
FUZZIFICAT ION
i
Fig. 5. Internal structure Fuzzy Logic Controller
inference engine.

Defuzzification
The output of the inference engine produces the output for the fuzzy set. This stage includes various methods that are used for producing the fuzzy set value for the output fuzzy variable T.
The method of defuzzification used in this is the centroid or centre of gravity to calculate the final fuzzy
n
value. In this the crisp output variable T is taken to be the geometric centre for the output fuzzy variable. The discretised universe of discourse is given by
Ti.outTi
n
T i1
outTi
i1
(14)
e
NB
NM
NS
ZE
PS
PM
PB
ce
NB
NB
NM
NM
NS
NS
NS
ZE
NM
NM
NM
NS
NS
NS
ZE
PS
NS
NM
NM
NS
NS
ZE
PS
PM
ZE
NS
NM
NS
ZE
PS
PM
PM
PS
NS
NS
ZE
PS
PS
PM
PM
PM
NS
ZE
PS
PS
PS
PM
PM
PB
ZE
PS
PS
PM
PM
PB
PB
TABLE I. RULE BASE FOR FLC BASED INDUCTION MOTOR DRIVE.
Fig. 7. Waveforms for the indirect vector control of induction motor using fuzzy logic controller
VII. CONCLUSION
T
qs
e
The reference electromagnetic torque * can be obtained by integrating the above equation and is further used to calculate i* . The membership functions which are defined
for the input and output variables are shown in Fig. 6. The rule base of the control technique is given in Table 1.


SIMULATION RESULTS
r
The simulation results are shown for the fuzzy logic controller based induction motor drive. The waveforms shown for the indirect vector control method based induction motor drive are the rotor speed, Nr and reference rotor speed, N* . Stator currents Iabc , stator line
voltage Vab , electromagnetic torque Te and load torque Tl . The reference speed is taken out to be 70 rad/sec. The rotor speed of the proposed fuzzy logic control based indirect vector control comes out to be almost smooth and no overshoot. The waveforms are shown on load and the reference speed is achieved in a very less time period with a very less steady state error.
Based on the study of indirect field oriented control of three phase induction motor a simulation model using Matlab/Simulink software package has been established. The simulation results show that the simulink model smoothly works over the fuzzy logic controller and has proper static and dynamic characteristics under normal working condition. The fuzzy logic controller proves to be more robust as compared with other conventional control techniques. The experimental results have shown the correctness and feasibility of control of the induction motors using fuzzy logic controller.
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