1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME
44
SIMULATION OF SRM USING FUZZY LOGIC
1
Kiran Srivastava, 2
B.K. Singh
1
RKGIT, Ghaziabad, India
2
Kumaon Engineering College, Dwarhat India
ABSTRACT
This paper presents the use of fuzzy logic for switched reluctance motor (SRM) speed. The
(Fuzzy Logic Control) FLC performs a PI-like control strategy, giving the current reference variation
based on speed error and its change. The performance of the drive system was evaluated through
digital simulations through the toolbox Simulink/ Matlab program Fuzzy controller and fuzzy logic
are generally non-linear systems; hence they can provide better performance in this case. Fuzzy
controller is mostly presented as a direct fuzzy controller or as a system, which realizes continued
changing parameters of other controller and the decision form of the fuzzy control is illustrated and
simulated.
Key words: Switched Reluctance Motor, Fuzzy Logic Controller, Simulation.
INTRODUCTION
The switched reluctance motor (SRM) has becoming an attractive alternative in variable
speed drives, due to its advantages such as structural simplicity, high reliability and low cost [1,2].
Many papers have been written about SRM concerning design and control [3]. An important
characteristic of the SRM is that the inductance of the magnetic circuit is a nonlinear function of the
phase current and rotor position. So, for the control and optimization of this drive, a precise magnetic
model is necessary. To obtain this model is not an easy task, because the magnetic circuit operates at
varying levels of saturation under operating conditions [4]. Further, the nonlinear characteristic of
this plant represents a challenge to classical control. To overcome this drawback, some alternatives
have been suggested in [5], using fuzzy and neuronal systems.
A PI Controller (proportional-integral controller) is a special case of the PID controller in
which the derivative of the error is not used. Fuzzy logic controller is an intelligent controller which
uses fuzzy logic to process the input. Fuzzy logic is a many valued logic which is much like human
reasoning.
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &
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ISSN 0976 – 6545(Print)
ISSN 0976 – 6553(Online)
Volume 5, Issue 6, June (2014), pp. 44-48
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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME
45
In this paper we present a study by simulation of the use of a FLC for SR drive. The SRM
simulated has a structure of eight poles on the stator and six on the rotor. The objective of the FLC is
to present a good performance.
STRUCTURE OF SRM
The Switched Reluctance Motor has gained significant interest in the field of industrial drive.
It has numerous advantages like simple and robust construction, reliability, low manufacturing cost,
high starting torque, high efficiency, and high speed capacity. The stator has concentrated windings
wound field coils and the rotor has no coils or magnets. The stator and rotor have salient poles;
hence, the machine is a doubly salient machine. Switched Reluctance Motor is a highly nonlinear
control plant and operates in saturation to maximize the torque output. The principle of operation is
such that the motion is produced as a result of variable reluctance in the air gap between the rotor
and the stator. When the voltage is applied to the stator phase, the rotor tries to rotate in the direction
of minimum reluctance position producing reluctance torque. In order to achieve a full rotation of the
motor, the windings must be energized in the correct sequence. The Switched Reluctance Motor
operates in all the four quadrants and it is suitable to operate in hazardous areas also [7].
Fig. 1: Structure of 4 phase 8/6 SRM
The voltage equation for SRM is given by,
V= r i +dΨ / dt , ψ=Li=Nφ …… (1)
For r = 0
V = L di/dt + i (dL /dθ) (dθ/dt)….. (2)
V = L di/dt + i ω (dL/dθ) …………. (3)
T = ½ i2
dL/dӨ…………………….. (4)
Where V is voltage, L is Inductance, r is resistance in winding, θ is rotor position, Ψ is flux linkages,
i current in each phase. This equation shows that the torque developed depends only on the

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME
46
magnitude of current & direction of dL/dӨ but independent of direction of current [8].
MOTOR SIMULATION
Fig. 2 shows a simulation diagram in Matlab- Simulink. In the simulation is thought SRM 8/6
making use for modeling a non-linearity called look-up table, which relatively truly matches a
nonlinear system. The fuzzy logic makes the parameter change on the basis of input current and
mutual position of rotor and stator pole. This is control method of SRM with current controller.
There are also included blocks in the regulation structure for determination the conduction of
individual motor phases [9], [10].
Fig. 2: SRM control structure in Matlab-Simulink
FUZZY LOGIC CONTROLLER
It is good to remind for introduction that the general logics was developed for the change of
parameters of PID controller. The fuzzy logics was used for the creating PID controller with
nonlinear setting of parameters K, TI, a TD for the reducing overshoot or acceleration of transient
effect. In this case the fuzzy controllers evaluates values of input. The value of current PI controller
parameters is changed according to set the rule base and function of pertinence in every step. The
rule of fuzzy logic may be in form:
IF current is small AND position is high
THEN output is small
The same results can be obtained, if you use the fuzzy PI controller with nonlinear setting.
Until you know the setting of PI controller parameters for an environment of the operating points in
which regulation system is. It can be selected the correct setting of controller parameters with help of
fuzzy supervisor. There is not to think only one complexion fuzzy PI controller due to rising severity
in these simulation cases and from practical overview. The fuzzy controller is with two inputs and
division ‘universe’ on 7 functions to needs 49 rules. When you have the same number of division
universe and you want to rise up the number of inputs for good description of nonlinear system with
4 inputs then the number of rule rise up to 2401. On this account it is important to combine more
fuzzy structures with inputs less than one fuzzy system with huge number of rule [11]. We can
describe fuzzy system by next equation:

4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME
47
output = D { interference{ F(input_current), F( input_position)} }
where F is representing fuzzification, D defuzzification. It was selected the variation which is
observing the same PI controller which is using superior adaptation fuzzy controller for the change
of parameters. The inner structure of adaptation block from Fig. 2, we can see in Fig. 3. It is clear,
that adaptation is performed in certain range of input values which have the influence for motion of
SRM. The outputs of block are signals corresponding to gain and time constant for classical PI
controller.
Fig. 3: Scheme of fuzzy controller for parameters setting
The fuzzy controller which has two inputs and one output too. We can set its nonlinear
behave with the aid of rule base. It is expert system, where the rule base entry is on the foundation of
knowledge and experience of an expert with system. The control surface is result of designed fuzzy
system.
The table 1 shows the rule data base
SIMULATION RESULT
Designed adaptation controller for parameters setting was verified on described mathematic
model. The courses introduced below in figures are achieved for changing applied current value from
30A to 50A. There are showed the phase current courses with corresponding logic signal value
which corresponds to leading specific phase applied time.
Fig.4: Phase current Ia time courses Fig 5: Phase current Ia time courses
with Iref = 30A with Iref = 50A

5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print),
ISSN 0976 – 6553(Online) Volume 5, Issue 6, June (2014), pp. 44-48 © IAEME
48
CONCLUSION
The result of designed system of current control with classic PI controller, which is observed
by fuzzy supervisor, is improved current courses during changing system parameters. The main
output of designed fuzzy supervisor for simple review of system non-linearity is so-called control
area which determines non-linearity of the system. Current control independence during changing
system parameters is shown in figures 4 and 5 of current courses. The independence is given by the
change of the PI controller parameters. As it was mentioned before, the main advantage of fuzzy
control is a possibility to create for the drive suitable control on basic rules. We can achieve better
control results because of the fuzzy systems non-linearity. It is confirmed by these simulation results.
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