A Fuzzy-PI based Gain Optimizing Approach for Speed Controlling of DC Motor

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 1 | Jan- 2023 www.irjet.net p-ISSN: 2395-0072
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A Fuzzy-PI based Gain Optimizing Approach for Speed Controlling of DC
Motor
Anshul Soni1, Deepak Bhataniya2
1M.Tech Student JIT Borawan, Khargone(M.P) INDIA
2Assistant Professor, Department of Electrical and Electronics JIT Borawan, Khargone(M.P) INDIA
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Abstract - In areas demanding flexible and smooth speed
torque characteristic, DC motor drives are very popular. Its
small size (in comparison with other motors of same rating),
high torque density and better efficiency makes it more
prevalent. This variation is speed is according to the demand
of load connected. Thus, this requires a system, which
automatically adapts to this point inshortmakingsystemvery
vital for engineering and science. In this work, thefocuswillbe
to develop a model based on two techniques called PID
controller and Fuzzy logic based intelligent controller. The
fuzzy controller is comprised of a human-like intuition
strategy with the help of membership function and rule base.
The design and implementation are done on MATLAB
platform. In this work, the comparison is done over the
performance of two controllers for DC motor basedonsettling
time, rise time, peak time, and percent overshoot. The inputs
taken for modeling the fuzzy logic model areerrorandchange
in error and the single output to the model will be required
signal for motor speed control. Based on the signal, the input
supply is regulated for speed control of DC motor.
Keywords– Speed Control of DC Motor, Fuzzy logic
controller, PID Controller Tuning, rise time, Peak
overshoot, Peak time.
1. INTRODUCTION
In the past century, the electric motor has played a major
role in industrial development. Several types of motor are
developed based on the requirement. Even in the domestic
application, motors have a big role to play. Several types of
appliances like AC, Refrigerators, fans, etc. uses electric
motors only. Every industrial process demands a variable
speed operation from the motor based on the requirement
from the load. Hence, to fulfill that demand a circuit is to be
designed to control the speed of the motor, known as an
electric drive. Out of all type drives, the DC motor is very
popular due to its simple and efficient operation along with
good control characteristics comparetotheinductionmotor.
In the present electrical framework pressure from
government regulations and reduction in operational costs
are high. The system demand that the framework is
independent of electromagnetic obstruction and good
productivity. One such activity is speed control in DC motor.
Any DC motor drive needs to have minimum possible
transients’ generation which controlling speed. Usually, the
armature voltage of the DC motor is controlled for varying
speeds. In any DC motor speed control drive, a reference
speed is chosen to have a benchmark to achieve. It is also
provided with the current speed of the motor in reference to
current armature voltage. The difference between both
signals is calculated to generate a control signal to change
armature voltage and thus motor speed.
This work has utilized two techniques to generate the
required signal for controlling the voltage of DC motor
armature. First is the conventional PID controller, having
constant gain throughout the operation. Second is the Fuzzy
logic controller, which works on the principle of the Fuzzy
logic hypothesis, which unlike Boolean rulewithonlytrueor
false state utilizes the entire framework between true and
false. Fuzzy logic controller optimizes gain for the PID
controller at every time step
DC motor is known to have a good speed-torque
characteristic to the framework as compared to other
motors of the same rating, hence is more popular is the area
where variable speed is demand. The issue comes with a
good controller with a DC motor to control its speed with
good accuracy.
2. DC MOTOR AND PID CONTROLLER OVERVIEW
The electric motor is a motor that changes over electrical
vitality into mechanical vitality. There are two kinds of the
motor which are AC motor, and DC motor. A straightforward
DC motor uses power and an attractive field for delivering
force that pivots the motor. DC motor beats to AC motor
since it gives better speed control on high force loads and
uses in wide mechanical applications. DC motors are
increasingly used as it intended to use with batteries and
solar cells vitality sources, which give convey ability where
we required it and in this manner give a financially savvy
arrangement, since it is absurd to expect to have an AC
power supply in each spot, DC motor show its reaction at
both voltage and current. The applied voltage portrays the
speed of the motor while the current in the armature
windings shows the force. Whenever the applied burden
expanded in the pole of the motor, at that point to continue
its speed motor draws progressively current from the
supply, and in the event that the supply can't give enough
current, at that point motor speed will be influenced.

By and large, it tends to be said that applied voltage
influences speed while force is controlled by the current. DC
motors give progressively compelling outcomes if the
cleaving circuit is utilized. Low power DC motor ordinarily
use in lifting and transportation purposes as low power AC
motors don't have great force ability. DC motor is utilized in
railroad motors, electric vehicles, lifts, automated
applications, vehicle windows, and a wide verity of little
machines and complex modern blending forms where force
can't be undermined.
There are a few kinds of DC motor however most normal are
brushed DC motor, brushless DC motor, stepper motor, and
servo motor. These DC motors have three winding
procedures, for example, shunt DC motor, arrangement DC
motor, and compound DC motor.
Fig-1: DC motor parts
2.1 DC MOTOR MATHEMATICAL MODELLING
Fig-2: Schematic representation of the considered DC
motor
Equation for the DC motor
𝐽𝑠2𝜃(𝑠) + 𝑏𝑠 𝜃(𝑠) =𝐾𝑡 𝑉(𝑠) − 𝐾𝑒𝑠 𝜃(𝑠)
𝐿𝑎𝑠 + 𝑅𝑎
Fig-3: DC Motor block diagram
It has been developed in MATLAB/Simulink as shown
Fig-4: DC Motor Modelling
2.2 PID CONTROLLER DESIGN
Every machine is prompt towards dynamic systems. Having
a controller is very important for maintaining stability.
Several conventional methods proposed in past, some of
which are listed 1)Classical control methods like root locus
design, lead-lag compensator, etc 2) Nonlinear methods like
back-stepping, Lyapunov redesign, etc 3) Control methods
having an adaptive property like self-tuning regulators,
dynamic programming, etc
All such controllers have a property to utilize the data
mathematical model of the provided system.Eachcontroller
is required to have a self-tuning capability of the system.
Such a method is more important because of the factthatthe
mathematical model designed is not always accurate as
depends upon the parameters chosen to designit.Heuristics
is one such problem arises with all conventional technique
and it doesn’t fit into the proper mathematical framework
which might help in proper controlling.
Out of conventional methods PIDcontrollersfindapplication
in speed controlling of DC motor. PID stands for
Proportional, Integral, and derivative modes of control. A
combination of these three different modes is utilized in
many systems. In the following section discusses three
modes in detail

The proportional controller mode is usually useful under
conditions when suddenly change in the system happened.
“P” controller is linear in nature hence its response is very
fast. This controller finds difficulty in stabilizing the higher-
order process hence can be effectively used for single order
processes only. Such a process system is comprised of only
one energy store element. Hence the system can withstand
large gain increment which further leads to low steady-state
error. A low steady-stateerrormeansthesystemisfollowing
the output pattern of the process efficiently. Also, large gain
gives a fast response, as discussed earlier means process
control will be very sensitive in a closed-loop system. Also,
proportional gain helps in attaining smaller amplitudes and
phase margin
Integral controller mode comes handy when the present
state of the dynamic process somehow dependsonthevalue
of the past state of the system.
Hence a combination of P& I called PI controller to finds the
better application. The benefit of PI is that it will remove
forced oscillation in the system that occurred due to
switching in the system. Although there is also a certain
drawback of the PI system, one major one is compromised
with speed. The response speed of PI bis slower than that of
P and also have a negative effect on overall stability.
Both integral and proportional modes of the controller have
no ability to predict the nature of the error in the near
future. This led to the requirement of a controller which can
provide information regarding values of error in the future
for a dynamic system, which is fulfilled by the derivative
controller. Derivative modes have the ability to find the
solution to gain whose value is dependent on the future
error value. Again, in order to avoid sudden variation in
output due to sudden variation in input combination of
proportional and derivative is used. Such a system finds its
applicatio0n in areas like submarines, ships, flying vehicles.
When all the modes are used simultaneously in anydynamic
system it is called a PID controller. The controllers help in
estimating control inputs of the system by adjusting gain
according to system error pattern.
Fig-5: Block Diagram of the PID controller
PID controller can achieve quick response with the help of
derivative mode. Reduction in error towards zero using
integral control mode and reduction is oscillation using
proportional mode. Such a controller is used with the order
of the dynamic system is more than 1st order. Such a
controller is used in an area where highprecisionisrequired
like Autopilot in Airplanes. proportional, integral, and the
derivative term is given by:
𝑃 = 𝐾𝑃 𝑒(𝑡)
t
𝐼 = 𝐾𝑖 ∫ 𝑒(𝑡)𝑑𝑡
0
𝐷 = 𝐾𝑑 𝑑𝑒(𝑡)
𝑑𝑡
Table-1 Effects of Coefficients
2.3 PID TUNING METHODS
Every dynamic system has its ownbehaviorandeachsystem
has its own requirement which depends on the adjustment
of control parameters to the best possible value. Tuning is
thus the process of finding the most optimal value of gain of
3 modes such that system controlling can happenalongwith
maintaining stability. Different methodsaretherefortuning,
some of them are manual tuning and PID tuning software
method.
The manual tuning method as the name suggests works by
changing values of Kp, Ki & Kd till the demand for control is
obtained. It's like the hit & trial method.Stepstofollow while
finding optimal gain value is to set the value of Kp is
increased until the output of the system start oscillations.
The value of Kp is exactly half of the above point. After
setting
Kp, the value of Ki increased until the error is settled
insufficient time until the system becomes unstable. After
setting Ki, the value of Kd is increased until the system
steady- state error is reduced to acceptable value with
having a system overshoot.

In the PID tuning software method, the gain is optimized
with the help of any theoretical method implemented with
the help of any software. In this work, we have used both
these methods, for optimizing gain. In the software tuning
method, we have used the fuzzy logic approach in MATLAB
software.
Fig-6: The block diagram of the PID closed-loop
control system
2.4 STEP RESPONSE CHARACTERISTICS
In order to understand the behavior of any system, step
response characteristics are used. In such an analysis, the
output of the system is an observer for a change in input
from 0 to 1 in a very short time
Fig-7: Step Response Characteristic
2.5 FUZZY LOGIC THEORY
Fuzzy logic is a mathematical systemthattakesinputinform
of crisp value and performs the operation using equivalent
linguistic terms. Basically, fuzzy logic isbasedontheway the
human brain behaves, where its decision is based on
feelings. The way the human brain takes a decision based on
past learning experiences is now replicated in fuzzy logic
theory.
In the year 1965, the fuzzy logic approach was first
proposed, but it was in the year 1974 when fuzzy logic was
first used in any practical application. It was Dr. Mamdani
(based on whom Mamdani approach of FLC is named) used
Fuzzy logic in controlling of the steam engine. Since then the
popularity of a fuzzy logic idea has gained pace and now it
finds application in each sector of society ranging from
automobiles, industrial manufacturing and hospitals, etc. in
order to implement fuzzy logic three-stage operations are
performed. It starts with fuzzification then fuzzy inference
process & defuzzification.
Any fuzzy set can be expressed in the following way
µ𝑥(𝐴) 𝜖 [0,1]
Where x is any set, A is any member of that set, µ�(�) is the
membership function. Now similar to classical set theory
fuzzy also exhibit an almost similar type of operations. Let x
& y are two fuzzy sets element
Union: 𝑥 𝑈 𝑦 = µ𝑥(𝐴) 𝑈 µ𝑦(𝐴)
= max (µ𝑥(𝐴), µ𝑦(𝐴))
Intersection: 𝑥 Ո 𝑦 = µ𝑥(𝐴) Ո µ𝑦(𝐴)
= min (µ𝑥(𝐴), µ𝑦(𝐴))
Compliment: 𝑥𝑐 = 𝐹/𝑥
2.6 INTRODUCTION TO FUZZYLOGICCONTROLLER
Any fuzzy logic controller performs three maintasksnamely
Fuzzification, Decision using control block, and
defuzzification. The followingblock diagramshowsthesteps
involved in FLC.
Fig-8: Block Diagram approach of FLC
2.7 FUZZY CONTROL RULES
In order to gain knowledge like a human being, rules are
formed in a fuzzy interface system with the help of the
expert opinion of human beings. Such rules are based on
input & output with the help of the IF-THEN condition.Rules
base help in developing an algorithm to give output for any

particular input value. These rules help FIS to calculate the
degree the i/p matches the condition of a rule. The process
of forming rules can be done in two ways. In the first
mapping between i/p & o/p is done with linguistic terms.
Whereas in a second way, logic is formed to find the
relationship between i/p & o/p.
2.8 DIFUZZIFICATION
The process is the opposite of fuzzification. Post fis, the o/p
generated is in the form of linguistic or vague form. In order
to utilize in the application, it needs to be converted back to
crisp o/p. Several methods are possible for defuzzification.
some of them are discussed below.
Mean of maximum (MOM) method: in this method, the
output value is equal to the average value of output with the
highest degree. Taking examples of AC again if the o/p FIS is
“SLOW” then the crisp value from the defuzzifier can be
given by the following equation.
MOM(SLOW) =∑ 𝑥𝘍𝗀 𝑝𝑥𝘍/𝑝
= {𝑥′|µ𝑆𝐿𝑂𝑊(𝑥′)
Where p stands for a set of o/p x with a maximum value of
MF for o/p SLOW.
Fig-9: MOM Method
3. FUZZY PI CONTROLLER
Fig-10: Fuzzy-PI control system block diagram
3.1 FLOW CHART FOR SPEED CONTROL OF DC
MOTOR USING FLC
The below figure represents thestepsinvolvedincontrolling
the speed of the DC motor using the fuzzy logic controller.
The work starts with the definition of inputs and outputsfor
the given problem. Now each of these inputs and outputs
must be represented in the form of respective membership
function with different range and different widthofdifferent
categories, making each membershipfunctionuniqueforthe
particular input parameter.
After defining the membership function the next step is to
develop a rule base using knowledgebasebyexpertopinion.
These rules are defined based on an if-then conditional
statement. Once these rules are formed a simulation is done
based on input and output data. Based on this data error is
calculated on forecasted and actual value of the output
parameter. After the evaluation of error, a comparison is
done with the required optimum error value. If the target is
reached then no further training is required and the process
will end, otherwise, further tuning of the process is done to
improve the result and the same steps are followed again.
Fig-11: Flow chart for the fuzzy logic model

3.2 VARIABLES USED
The Linguistic Variables considered in the control are
Z - Zero
PM - Positive Medium
NM - Negative Medium
PL - Positive Large
NL – Negative Large
We have considered 5 Linguistic variables (NL, NM, ZE, PM,
PL) for the Input ‘Error’ and only 2 Linguistic variables (NL,
PL) for the Input ‘Rate of Change in Error’. In the case of
Output ‘Control’, we have considered 5 Linguistic variables
(NL, NM, ZE, PM, and PL).
3.3 MEMBERSHIP FUNCTIONS USED
The inputs are Error is speed and Change in Error. We have
studied above the Fuzzy rule base andformedthe rulesfor5,
2 linguistic variables of the inputs and 5 linguistic variables
of the output using the Fuzzy toolbox. The membership
functions used are
Error Signal – 2 trapezoidal and 3 triangular.
Change in Error signal – 2 triangular.
Control signal – 5 triangular
Fig-12: Fuzzy Logic Designer
3.4 FUZZY RULE BASE
General Interpretation of the control rules to be set to the
Fuzzy control:
If Error = 0 and Change in error = 0, then do not change the
present setting.
If Error is non-zero but Error is tending to zero at an
acceptable rate, then do not change the present setting.
If Error is increasing or decreasing, then make the control
signal according to the magnitude and sign of the Error and
Change in Error to make the Error = zero.
Fig-13: Fuzzy Rule Viewer
4. RESULTS
The Simulink model is prepared for two techniques, and
their results obtained are shown and discussed in detail.
Post mathematical modeling of DC motor, it is implemented
in Simulink. The model of the DC motorremainsthesamefor
both the controllers. The controllers are then designed in
Matlab/Simulink software. Both the Fuzzy and PID based
model’s output is displayed and compared to find the best
out of two based on speed signal response parameters
4.1 PID CONTROLLER BASED SPEED CONTROL
MODEL
Fig-14: DC motor Simulink model

The model comprises four factors of DC motor they are
Armature Resistance, Armature Inductance, Inertia, and
damping coefficient.
Rated Speed = 1500 rad/sec., Back EMF constant = 0.01
V/rad/sec , Reference Speed = 1450 rad/sec. and Load
Torque = 10 N-m.
The above figure 15 shows the model of the PID controller.
The reference speed was set to 800 for the first 5 sec and to
1450 for the last 5 sec. The reference speed is normalized to
per unit value. This value is then subtractedwiththepresent
output value of the speed of the DC motor, giving error. The
value of the error is fed to the PID controller, which
generated the appropriate signal to reduce the error value
and control the speed of the motor. The response of the
motor based on the controller is noted and shown in the
following figures
Fig-15: Simulink model of speed control of DC motorusing a
PID controller
Fig-16: Reference speed signal for the PID controller
Fig-17: Error in Speed response using a PID controller
Fig-18: PID controller output signal

Fig-19: per unit Speed Response signal using a PID
controller
Fig-20: Speed Response signal using a PID controller
4.2 FUZZY LOGIC CONTROLLER BASED SPEED
CONTROL MODEL
Fig-21: Simulink Model for Fuzzy Logic Control
Figure 20 shows the model of the Fuzzy logic controller. The
reference speed was set to 800 for the first 5 sec andto1450
for the last 5 sec. The reference speed is normalized to per
unit value. The advantage of per-unit value is that it makes
designing a range of variables for membership function in
the Fuzzy logic toolbox easy. This value is then subtracted
with the present output value of the speed of the DC motor,
giving error. The value of error is further differentiated
using a derivative block to get a change in error value. The
value of error and change in error is fed to the Fuzzy
controller block as inputs, which then generated the
appropriate signal to reduce the error value based on the
rule base. This output value is then subtracted with
armature voltage to control the speed of the motor.

The response of the motor based on the controller is noted
and shown in the following figures. The initial load torque
(TL) taken in the present case is 0.0001 N-m and 0.002 N- m
as the final value of load torque after 7 sec. The value of the
fuzzy logic output signal and Load toque is fedasinputtothe
motor model, which will give per unit value of speed. The
speed is then multiplied with inverted gain as taken with
reference speed gain block to get the actual value of speed.
Fig-23: Error in Speed response using FLC
Fig-22: Reference speed for FLC
Fig-24: Change in Error in Speed response using FLC
Fig-25: Fuzzy logic controller output response

Fig-26: Per-Unit Speed Response using Fuzzy
controller
Fig-27: Speed Response using Fuzzy controller
FUZZY CONTROLLER
Table-2 speed response parameters for PID Controller
Rise Time: 5.2730 sec
Settling Time: 6.2274 sec
Settling Min: 1305.1 m/sec
Settling Max: 1501.3 m/sec
Overshoot: 3.5366 rad/sec
Undershoot: 2.2372 10^-05 rad/sec
Peak: 1501.3 m/sec
Peak Time: 5.8704 sec
Table-3 speed response parameters for fuzzy controller
5.CONCLUSION
In this work, based on the mathematical modeling of the DC
motor, the Simulink model of DC motor is been successfully
designed. Differentcontrollersarestudiedforcontrolling the
speed of the DC motor. Out of which PID has stood out as a
convention solution. We have also studied and understood
various concepts of Fuzzy logic and Fuzzy set theory. The
fuzzy logic controller is proposed as a better supplement to
PID. After that, both controllers are designed and connected
with the above DC motor model in Matlab/Simulink
successfully. The simulation is performed on both models
separately and their results are obtained and summarized.
On comparing results in table 2 and table 3 it is clearly
Rise Time: 5.2030 sec
Settling Time: 5.5475 sec
Settling Min: 1291.6 m/sec
Settling Max: 1436.5 m/sec
Overshoot: 0.0916 rad/sec
Undershoot: 1.044310^-04 rad/sec
Peak: 1436.5 m/sec
Peak Time: 5.7453 sec
4.3 SPEED RESPONSE PARAMETERS FOR PID AND
evident that Fuzzy logic-based controllers are better
performing than PID based controller because it has a low
value of rising time, peak time and settling time and also has
a small value of overshoot.

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