speed control of separately excited dc motor using fuzzy PID controller(FLC).In this research, speed of separately excited DC motor is controlled at 1500 RPM using two approaches i.e. PSO PID and fuzzy logic based PID controller. A mathematical model of system is needed for PSO PID while knowledge based rules obtained via experiment required for fuzzy PID controller . The conventional PID controller parameters are obtained using PSO optimization technique. The simulation is performed using the in-built toolbox from MATLAB and output response are analyzed. The tuning of fuzzy PID uses simple approach based on the rules proposed and membership function of the fuzzy variables. Design specification of fuzzy logic controller (FLC) requires fuzzification, rule list and defuzzification process. The FLC has two input and three output. Inputs are the speed error and rate of change in speed error. The corresponding outputs are Kp, Ki and Kd. There are 25 fuzzy based rule list. FLC uses mamdani system which employs fuzzy sets in consequent part. The obtained result is compared on the basis of rise time, peak time, settling time, overshoot and steady state error. PSO PID controller has fast response but slightly greater overshoot whereas fuzzy PID controller has sluggish response but low overshoot. The selection can be done on the basis of system properties and working environment conditions. PSO PID can be used where the response desired is fast like robotics where as fuzzy PID can be used where desired operation is smooth like industries.

1. A Presentation
on
Speed Control of DC Motor using PID
FUZZY Controller
Supervised By: Er. Shahbuddhin Khan
Paschimanchal Campus,IOE
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Paschimanchal Campus, IOE
Department of Electrical Engineering
Presented By:
Anil Acharya (BEL/071/202)
Bikash Kumar Pal (BEL/071/209)
Binod Kafle (BEL/071/211)
Madan Rimal (BEL/071/220)

2. Presentation Overview
 Introduction
 Objectives
 Literature Review
 Methodology
 DC motor parameter and modelling
 Ziegler-Nichols tuning method
 Fuzzy Logic controller design
 MATLAB Simulation and Result
 Conclusion
 Research Gap/Further Recommendation
 References
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3. 1. Introduction
 For better performance of speed control, Proportional Integral Derivative controller (PID)
is widely used in industrial controller due to its control loop feedback mechanism.
 PID parameters are calculated by Zeigler-Nichols’ empirical formula.
 Major problem in applying conventional control algorithm in a speed controller are non
linear characteristics such as saturation and friction which could degrade the performamce.
 Fuzzy logic control (FLC) is knowledge based controller which continuously tunes output
variables.
 Fuzzy sets are sets whose elements degrees of membership.
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4. 2. Objectives
 To design a conventional PID controller for speed control of DC motor.
 To design a Fuzzy logic controller as another type of controller that can be used to control
speed of DC motor.
 To analyze the performance comparison between conventional PID and Fuzzy logic
controller in order to control speed of the DC motor based on MATLAB simulation.
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5. 3. Literature Review
 Fuzzy logic control is the application of fuzzy inference process automation.
 FLC has fuzzy input, fuzzification, rule list, defuzzification and fuzzy output set.
 Fuzzification converts input data into suitable linguistic values that may be viewed as
labels of fuzzy sets.
 Defuzzification is applied to all actions that have been activated are combined and
converted into a single output control signal.
 Knowledge based FLC will relate the input variables to the output varialbes using If-Then
statements.
If (antecedent) Then (consequence)
For example:
If pressure is high, then volume is small.
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6. Continued…
Basic terminology in Fuzzy logic
 The degree of membership(μ) is the degree to which a crisp variable belongs to a fuzzy set.
It is expressed either as fractional value ranging from 0 to 1 or percentage ranging from 0%
to 100%.
 A membership function(MF) is normally expressed graphically and tends to illustrate how
completely a crisp variable belongs to a fuzzy set. Generally shape of MF are: trapezoidal,
triangular, gaussian etc.
 A crisp variable is a physical variable that can be measured through instruments such as
temperature.
 A linguistic variable is a variable that can take words in natural language as its values.
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7. 4. Methodology
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Figure: Flowchart of project methodology

8. Continued…
 Simulation is done using
MATLAB/SIMULINK from MATLAB
R2015a version.
 Fuzzy logic controller is designed by
MATLAB toolbox.
 In this project PID controller techniques
are used using Z-N algorithm and
Mamdani technique is used for FLC. At
the end comparison of settling time, rise
time, peak time, overshoots and steady
static error are shown between PID
controller and FLC.
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Figure: Fuzzy Logic Designer

9. 5. DC Motor Parameter And Modelling
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Parameter Symbol Value
Moment of Inertia of Rotor J 0.1 Kgm2
Damping constant B 0.008 Nm/rad/s
Armature resistance R 0.5 ohm
Armature inductance L 0.02 H
Back emf constant Kb 1.25 V/rad/s
Motor Torque constant KT 1 Nm/A
Armature voltage Va 200 V
Rated Speed N 1500 rpm
Table: Parameters of DC motor
Plant TF =
𝑤(𝑠)
𝑣(𝑠)
=
𝐾𝑡
𝑅+𝐿𝑠 𝐽𝑠+𝐵 +𝐾𝑏𝐾𝑡
=
500
𝑠2+25.08𝑠+627

10. 6. Ziegler-Nichols Tuning Method
 After finding the transfer function of the system, it becomes vital to find PID parameters.
Z-N algorithm is one of the powerful algorithm to tune and to find Kp, Ki, Kd parameters.
 Z-N algorithm deals with ultimate time(T), and delay time(L). So it is necessary to find the
response of the system without any controller.
 A tangent line is drawn at inflation point(63% of final value).
 The empirical formula for PID parameter tuning using Z-N method is:
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Controller Kp Ki Kd
P T/L Zero Zero
PI 0.9(T/L) L/0.3 zero
PID 1.2(T/L) 2L 0.5L
Table: Z-N PID tuning empirical formula

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Kp=1.2T/L=3.375
Ki=2L=0.032
Kd=0.5L=0.008
PID(s)=3.375+0.032/s+0.008s
Figure: Response of the system without controller

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Figure: Simulation Block Diagram of the system

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Figure: Output Response of PID controller and without
controller
Parameter Without
Controller
PID
Controller
Rise Time 0.0665 sec 0.0525 sec
Setting
Time
0.4sec 0.35 sec
Peak Time 0.1 sec 0.06 sec
Max. %
Overshoot
28.26% 25.81%
Steady
Static Error
55.6% 26.9%
Table: PID controller vs without
controller parameters

14. 7. Fuzzy Logic Controller Design
 FLC has two input and three output. Each input has five membership functions and each
output has seven membership functions.
 In this FLC design shape of membership function is triangular(trimf).
 Inputs are speed error and rate of change of speed error and outputs are Kp, Ki and Kd.
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Figure: The structure of self tuning fuzzy PID controller

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Figure: Fuzzy PID Designer

16. 7.1 Fuzzy sets of speed error(e) variable
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Figure: Membership function for input variable error

17. 7.2 Fuzzy sets of rate of change of error(de)
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Figure: Membership function for rate of change of input error

18. 7.3 Fuzzy sets of Kp
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Figure: Membership function for Kp output variable

19. 7.4 Fuzzy sets of Ki
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Figure: Membership function for Ki output variable

20. 7.5 Fuzzy sets of Kd
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Figure: Membership function for Kd output variable

21. 7.6 Rule List
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Figure: Rule list for PID parameter tuner

22. 7.7 Rule Surface Viewer using MATLAB
Toolbox
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Figure: Rule surface viewer of Kp, Ki and Kd respectively

23. 8. MATLAB Simulation and Result
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Figure: Simulink model for speed control of DC motor using Fuzzy PID controller

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Figure: Speed vs Time response of fuzzy PID controlled DC motor

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Figure: Error vs Time response of Fuzzy tuned PID controller with DC motor

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Figure: Rate of change of speed error vs Time response of fuzzy PID controlled DC motor

27. 8.1 Comparison
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Figure: Comparison between output response of DC motor with various controller

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28 Parameter Without Controller PID Controller Fuzzy PID
Controller
Rise Time 0.0665 sec 0.0525 sec 0.09 sec
Setting Time 0.4sec 0.35 sec 2 sec
Peak Time 0.1 sec 0.06 sec 0.79 sec
Max. % Overshoot 28.26% 25.81% 13.95%
Steady Static Error 55.6% 26.9% 0%
Table: PID controller vs without controller parameters vs Fuzzy PID controller

29. 9. Conclusion
 We have studied basic definition and terminology of fuzzy logic and fuzzy set. This project
introduces a design method of two inputs and three outputs self tuning fuzzy PID controller
and make use of MATLAB fuzzy toolbox to design fuzzy controller.
 Fuzzy controller adjusted the Kp, Ki and Kd gains of the PID controller according to speed
error and rate of change in speed error.
 From the simulation result fuzzy PID controller has small overshoot, small steady state
error, fast rise time in both transient and steady state.
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30. 10. Research Gap/Further Recommendation
 This fuzzy PID controller has still above 10% overshoot. The parameter of fuzzy PID
controller can be tuned by optimization technique such as Genetic Algorithm(GA).
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31. 11.References
 K. J. Astrom, T. Hagglund, Automatic Tuning of PID Controllers, Instrument Society of
America, USA, 1998.
 L. Reznik, Fuzzy Controllers, BH, Victoria University of Technology, Melbourne,
Australia, 1997.
 P. Vas, Artificial-Intelligence-Based Electrical Machines and Drives, Oxford University
Press, New York, 1999.
 R. Palm, D. Driankov, H. Hellendoorn, Model Based Fuzzy Control, Springer, Berlin,
1997.
 C.H. Chen, Fuzzy Logic and Neural Network Handbook, McGraw-Hill, United States,
1996.
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32. THANK YOU
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