International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 3 - May 2014
Implementation of Fuzzy Logic Controller for Speed
Control of Motor using VHDL
Shri Om Mishra, Shailendra Kumar, Prof.(Dr.) S.H.Saeed
M.Tech Student, Jr.Associate Professor, Professor and Head of Department, ECE , Integral University Lucknow
Integral Univesity , Dasauli ,Kursi Road , Lucknow-226026,India
Abstract— A DC motor relies on the fact that like magnet poles
repel and unlike magnetic poles attract each other. A coil of wire
with a current running through it generates an electromagnetic
field aligned with the center of the coil. These motors are
generally controlled using a three phase power semiconductor
bridge. The level of performance can be achieved by suitable
speed controllers in the motor. Generally speed control is
achieved by using proportional-integral (PI) controller in case of
permanent magnet motors. Although conventional PI controllers
are widely used in the industry due to their ease of
implementation and simple control structure, these controllers
pose difficulties where there are some control complexity such as
nonlinearity, parametric variations and load disturbances.
Moreover PI controllers require precise linear mathematical
models. But the fuzzy logic controller (FLC) resolves these
problems. In this paper I have worked on Fuzzy logic control
(FLC) for DC motor control applications due to its advantage
like nonlinearity, handling features and independence of plant
modeling. I am simulating and synthesizing the FLC model using
Xilinx ISE 12.4 and Modelsim 10.2a respectively using VHDL.
Implementation of a Fuzzy Logic Controller (FLC) using VHDL
for DC motor speed control is presented in this paper.
Keywords— Fuzzy Logic Controller (FLC), VHDL, Model Sim .
I. INTRODUCTION
In recent years, fuzzy control has emerged as a practical
alternative to classical control schemes when one is interested
in controlling certain time varying, non-linear, and ill-defined
processes. Usually Fuzzy controller involves a mathematical
description of the relation among inputs to the process, its
state variables, and its output. This description is called the
model of the system. Fuzzy controllers are used to control
consumer products, such as washing machines, video cameras,
and rice cookers, as well as industrial processes, such as
cement kilns, underground trains, and robots. Fuzzy control is
a control method based on fuzzy logic. Fuzzy logic can be
described as computing with words rather than numbers and
fuzzy control can be described as control with sentences rather
than equations. A fuzzy controller can comprise empirical
rules that are particularly useful in operator controlled plants
[1]. Fuzzy logic controller (FLC) is capable of improving its
performance in the control of a nonlinear system whose
dynamics are unknown or uncertain. Fuzzy control is similar
to the classic closed-loop control approaches but differs in that
it substitutes imprecise, symbolic notions for precise numeric
measures. The fuzzy controller takes input values from the
real world. These crisp input values are mapped to the
linguistic values through the membership functions in the
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fuzzification step. A set of rules that attempt to match the
decision making process of the human expert controlling the
system is then applied using certain inference mechanisms to
determine the output. Finally, the output is mapped into crisp
control actions required in practical applications in the defuzzification step [2]. Usually, linguistic variables hold values
that are uniformly distributed between 0 and 1, depending on
the relevance of a context dependent linguistic term [3].The
collection of rules is called a rules base and the rules are in the
familiar if-then format and formally the if-side is called the
condition and the then-side is called the conclusion. The
computer is able to execute the rules and compute a control
signal depending on the measured inputs error and change in
error. Therefore the rules reflect the strategy that the control
signal should be a combination of the reference error and the
change in error. Fuzzy inference is the process of formulating
the mapping from a given input to an output using fuzzy logic.
The process of fuzzy inference involves membership
functions. There are two types of fuzzy inference systems that
can be implemented in the fuzzy logic toolbox which are
Mamdani-type and Sugeno-type
A. .Fuzzification
Which converts controller inputs into information that the
inference mechanism can easily uses to activate and apply.
B. Rule-Base
(A set of If-Then rules) which contains a fuzzy logic
quantification of the expert’s linguistic description of how to
achieve good control.
C. Inference Mechanism
(Also called an”inference engine” or “fuzzy inference”
module), which attempt to match the decision making in
interpreting and applying about how best the control the
system.
D. Defuzzification
For the process, it converts the conclusions of the inference
mechanism into actual inputs for the process.
II INTRODUCTION OF DC MOTOR
Everyone recognizes the vital role played by electrical motors
in the development of industrial systems. There are five major
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International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 3 - May 2014
types of D.C motors in general use, which are the separately
excited D.C motor, the shunt D.C motor, the permanent –
magnet D.C motor, the series D.C motor and the compound
D.C motor. The D.C. machine is the first practical device to
convert electrical power into mechanical power, and vice
versa. Inherently straightforward operating characteristics,
flexible performance and efficiency encouraged the use of
D.C. motors in many types of industrial drive application.
Most multi-purpose production machines benefit from
adjustable speed control, since frequently their speeds must
change to optimize the machine process or adapt it to various
tasks for improved product quality, production speed [4]. All
D.C. drives use power semiconductor devices to convert and
control electrical power. A.C/D.C converters serve to obtain
variable D.C voltages from a constant A.C voltage and one
application is to use the D.C source to drive a D.C motor in
variable speed modes [5].
III PROPORTIONAL INTEGRAL DIFFERENTIAL
(PID) CONTROLLER
Many industrial controllers employ a proportional, integral
plus differential PID regulator arrangement that can be fitted
to optimize a particular control system. The PID controller can
be used in most control loops in an electrical drive.
The control action in a PID controller combines proportional,
integral and derivative control modes as shown in Fig 1
Integral
Differential
Process
+
Summin
g
A. Fuzzification
The first step in designing a fuzzy controller is to decide
which state variables represent the system dynamic
performance must be taken as the input signal to the controller.
Fuzzy logic uses linguistic variables instead of numerical
variables.
Fuzzy inference is the process of formulating the mapping
from taken input to an output using fuzzy logic. Mainly two
types of fuzzy inference systems can be implemented in the
Fuzzy Logic Mamdani-type and Sugeno-type.
B. Defuzzification
The reverse of Fuzzification is called Defuzzification. The
use of Fuzzy Logic Controller (FLC) produces, required
output in a linguistic variable. According to real world
requirements, the linguistic variables have to be transformed
to crisp output. Centre of gravity method is the best wellknown defuzzification method. Sugeno type of defuzzification
method is adopted in this work.
V FUZZY LOGIC CONTROL OF THE DC MOTOR
The fuzzy logic controller was applied to the speed loop by
replacing the classical polarization index (PI) controller. The
fuzzy logic controlled motor drive system block diagram is
shown in Fig 2
Proportiona
l
e (t) Error
signal
The fuzzy logic controller has three main components:
1. Fuzzification
2. Fuzzy inference
3. Defuzzification
u
(t
)
Power
supply
Fuzzy
Logic
Controller
DC
Motor
Fig 1 Proportional, Integral plus Differential Arrangement Controller.
The proportional, integral, differential PID controller output
equation is given as:
u(t) = KP e(t) + Ki
A/D
Converter
Vref
Sensor
+ Kd
Fig 2: Overall block diagram of speed control of DC motor
IV FUZZY LOGIC CONTROLLER
Fuzzy logic does not use mathematical equations, it
represented by operational laws in linguistics terms. Many
systems are too complex to model accurately even with
complex mathematical equations; therefore traditional
methods become infeasible in these systems. However fuzzy
logics linguistic terms provide a feasible method for defining
the operational characteristics of such system.
Fuzzy logic controller can be considered as a special class of
symbolic controller.
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Graphical representation of the membership functions of input
and output variable are shown in Fig 3. , Fig 4 and Fig 5.
Fig 3: Membership function of input variable
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International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 3 - May 2014
Fig 4: Membership function of input variable
ΔE be the linguistic variable for the change of error Δ e
U be the linguistic variable for the control output u.
The maximum range of motor is +/- 1500 rpm. The possible
error range is -750 to 750.The universe of Discourse of
change in error is based on the experiment data from the
conventional controller which gives the range of error is +/150.Ouput of the controller ranges from +/- 8.Linguistic
variables for E , ΔE , U are defined as
LE={Negative Big(-750rpm), Negative(-500rpm), Zero(0),
Positive(500rpm). Positive Big(750rpm)}
LΔE={Negative Big(-150rpm), Negative(-75rpm). Zero(0),
Positive(75), Positive(150)}
LU={Negative Very Big(-8), Negative Big(-6), Negative(-4),
Negative Small(-2), Zero(0), Positive Small(2), Positive(4),
Positive Big(6), Positive Very Big(8)}
Fig 5: Membership function of output input
VI Fuzzy Rule Set

























Fig 6 shows a block diagram demonstrating the
implementation of the FLC in a Speed control of DC motor .In
this application, the input interface converts the output of the
speed sensor into error and change of error which are used as
the two inputs to the FLC.
VR
1. If (X1 is NB) and (X2 is NB) then (U is NVB) (1)
2. If (X1 is NB) and (X2 is N) then (U is NB) (1)
3. If (X1 is NB) and (X2 is Z) then (U is N) (1)
4. If (X1 is NB) and (X2 is P) then (U is Ns) (1)
5. If (X1 is NB) and (X2 is PB) then (U is Z) (1)
6. If (X1 is N) and (X2 is NB) then (U is NB) (1)
7. If (X1 is N) and (X2 is N) then (U is N) (1)
8. If (X1 is N) and (X2 is Z) then (U is Ns) (1)
9. If (X1 is N) and (X2 is P) then (U is Z) (1)
10. If (X1 is N) and (X2 is PB) then (U is PS) (1)
11. If (X1 is Z) and (X2 is NB) then (U is N) (1)
12. If (X1 is Z) and (X2 is N) then (U is Ns) (1)
13. If (X1 is Z) and (X2 is Z) then (U is Z) (1)
14. If (X1 is Z) and (X2 is P) then (U is PS) (1)
15. If (X1 is Z) and (X2 is PB) then (U is P) (1)
16. If (X1 is P) and (X2 is NB) then (U is Ns) (1)
17. If (X1 is P) and (X2 is N) then (U is Z) (1)
18. If (X1 is P) and (X2 is Z) then (U is PS) (1)
19. If (X1 is P) and (X2 is P) then (U is P) (1)
20. If (X1 is P) and (X2 is PB) then (U is PB) (1)
21. If (X1 is PB) and (X2 is NB) then (U is Z) (1)
22. If (X1 is PB) and (X2 is N) then (U is PS) (1)
23. If (X1 is PB) and (X2 is Z) then (U is P) (1)
24. If (X1 is PB) and (X2 is P) then (U is PB) (1)
25. If (X1 is PB) and (X2 is PB) then (U is PVB) (1)
VII PI-LIKE FUZZY CONTROL
At this stage, the control law in (8) is not in fuzzy terms. In
order to design a fuzzy controller based on the PI control
structure, the following definitions are made:
Let E be the linguistic variable for the error e
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x1
FLC
y
x2
EF
+
- -
+
Z
1
Δ
u
D
C
M
oto
r
V
dc
Rul
e
bas
e
Fig 6: Overall block diagram of FLC based control system with Dc motor
Another interface converts output into the required value for
the plant. The characteristics of the interfacing blocks can be
described by the following equations:
Input interface:
e = VREF - Vdc ; x1=e; x2= x1 - x1z-1
Output interface:
Δu=y
VIII SIMULATION RESULTS
A. Simulation Results Using VHDL
In this paper I have implement fuzzy logic controller for speed
control of DC motors Here I have checked the input range of
voltage reference to get the desired voltage output. There is
some another inputs reset and clock. My project will work on
positive edge of clock and low reset. If reset is high all output
will be zero. Here for the input range (-8 to 0) volt, I am
getting output voltage -8 volt shown in fig 7 volt.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 3 - May 2014
Fig 9: Main RTL
Fig 7: simulation Result for input range ( -8 to 0) volts.
Here for the input range (0 to 8) volt, I am getting output
voltage 8 volt shown in fig 8 volt.
Fig 10: Internal RTL
Fig 8: simulation Result for input range ( -8 to 0) volts.
B. Synthesis Result
Fig 9 shows the RTL of our code, fig 10 shows the internal
RTL and fig 11 shows the devices utilized in my work.
Fig 11: Device Utilization
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International Journal of Engineering Trends and Technology (IJETT) – Volume 11 Number 3 - May 2014
IX. CONCLUSION
In my project, My focus is to develop a Fuzzy Logic Based
Controller so as to achieve precision in control. The controller
attempts to attain a certain level of human intelligence by
utilizing the linguistic variables instead of numerical ones. Its
main advantage is that it completely avoids the mathematical
computations, which relieves the designer from using
cumbersome techniques. All a fuzzy logic controller needs a
set of if-then rules and a knowledge base and which can be
easily provided by the programmer. The implementation of
the fuzzy logic controller is very straight forward by coding
each component of the fuzzy inference system in VHDL
according to the design specifications. The design of the FLC
is highly flexible as the membership functions and rule base
can be easily changed with reduced rule techniques. Because
of the reduced rule techniques the computation time of the
fuzzy controller is reduced and Speed Controlling can be done
for multiple motors of same specification. Hence it becomes
simpler to implement fuzzy logic for the design of controllers
for higher order systems. In my project, I have designed a
Fuzzy Logic Controller to be utilized in the speed control on
DC motor. The designing has been done with the help of
VHDL. This controller takes in crisp inputs, viz. speed error
(e) and change in error (∆e) and gives an output called change
in control. The output changes according to the rules. These
have been verified with the help of Modelsim Simulation
10.2a and for synthesis we have used Xilinx 12.1.
X. ACKNOWLEDGEMENT
determination in accomplishing this work. I wish to
express my deep sense of gratitude and profound
thanks to Mr. Shailendra Kumar (Jr.Associate
Professor), Integral University, Lucknow for
providing me the opportunity to work on this topic
under his guidance. The encouragement and support
he provided throughout, despite his busy schedule. His
feedback, constructive criticism and encouragement
were the driving force behind the successful
completion of this dissertation. I would also like to
offer my thanks to Head of The Department Prof (Dr.)
S.H. Saeed, Department of ECE, Integral University
for their throughout guidance and support whenever
required. I would also grateful to the author and
scholars whose work I have referred as a guiding
stone in my dissertation. Finally I would like to
express my sincere Gratitude to my friends, seniors,
parents, family and well wishers for extending help to
carry out the dissertation work.
REFERENCES
[1] Shepherd, W., 1998, "Power Electronics and Motor Control", Cambridge
University press, 1’st edition, Book.
[2] Yehliang, H., 2005, "A Fuzzy Proportional-Derivative Controller for
Engineering Optimization Problems Using an Optimality Criteria Approach",
Engineering optimization, IEEE Trans. on Optimization, Vol. 37, No. 6, pp.45
– 55.
[3] Sivanandam,S. and Deepa, S., 2007," Introduction to Fuzzy Logic using
MATLAB", Springer - Verlag Berlin Heidelberg .
[4] Ahmed R., 2007," Fuzzy Logic in Electric Drives", Lubbock Engineering,
Howard University, Washington, D.C., USA.
[5] Krishna, R.,1992," Criteria For The Comparison of Motor Drive Systems
in Motion
Control", Proceedings of International Conference on
Intelligent Control and
Instrumentation, Vol.1, No. 3,pp.127 – 133.
First of all I am grateful to God, The most beneficent
and merciful who provides me confidence and
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