 Open Access
 Total Downloads : 1774
 Authors : Ms. R. P. Suradkar, Dr. A. G. Thosar
 Paper ID : IJERTV1IS8489
 Volume & Issue : Volume 01, Issue 08 (October 2012)
 Published (First Online): 29102012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Enhancing The Performance Of DC Motor Speed Control Using Fuzzy Logic
Ms. R. P. Suradkar Dr. A. G. Thosar
Student, M .E. EPS Principal
Government Engineering College MSSs College of Engineering and Technology Aurangabad, India. Jalna, India.
Abstract
This paper focuses on speed control of DC motor by conventional PI controller and Fuzzy Logic Controller (FLC). The DC motor is modelled using the state space equations, which are used to create the SIMULINK models of the above methods. The PI controller reduces the steady state error measured between motor speed (r) and reference speed (ref), while the Fuzzy Logic Controller (FLC) adjusts fuzzy membership functions to control the motor speed. The response of these methods is compared with each other.

Introduction
In the industries, the speed of DC motor is controlled to perform different task and for load changes. The speed control can be achieved by using conventional method as PI controller or Fuzzy controller [1,2].
In PI controller, the objective is to make the motor speed follow the reference speed. Thus PI controller is used to reduce or eliminate the steady state error measured between motor speed r reference speed ref [3].
Fuzzy logic is used where the system is
Fig.1: DC motor speed control using PI Controller While modelling a DC motor, it can be
described by state space equations.
Where,
= armature current
= motor speed in rad/s
= voltage applied to armature circuit
nonlinear and difficult to calculate mathematical model. Intelligent techniques like Fuzzy logic developed to replace conventional control techniques. Thus, the effects of nonlinearity in conventional methods are reduced using Fuzzy logic. This fuzzy controller is similar to PI controller, when used with the defuzzification method [4,5].
The fuzzy membership functions and rules are modified after applying to PI controller. The output of fuzzy controller is created by rules composed of two inputs and linguistic definitions. The output of FLC can be improved by varying fuzzy membership functions and rules [7].
220
200
Motor Speed rad/sec
180
160
140
120
DC Motor Speed Control using PI Controller

DC motor speed control using PI controller
The PI controller is used to control the speed of DC motor by reducing the steady state error measured between motor speed (r) and reference speed (ref). The PI controller with DC motor is as shown in block diagram.
100
PI Controlled Response
Reference Input
2.5 3 3.5 4 4.5 5
Time in Sec
Fig.2: DC motor speed response with PI controller DC Motor Parameters used for the model
are as shown in the Table1.
Table 1: DC Motor Parameters
Parameters
Description
Value
Ra
Armature resistance
4.67
La
Armature
inductance
170e3 H
J
Moment of inertia
42.6e6 Kgm
f
Viscous friction
coefficient
47.3e6Nm/rad/se
K
Torque constant
14.7e3Nm/A
Kb
BackEMF constant
14.7e3Vsec/rad

DC motor speed control using Fuzzy Logic
Instead of using system model, FLC operation based on heuristic knowledge and linguistic description is used. The lack of knowledge of developing membership functions and rules can give wrong results. Thus with sufficient knowledge of adjusting the rules and membership functions the performance of FLC can be improved .The design procedure of FLC contains three steps as

Defining input and output

Defining membership functions and rules

Adjusting membership functions and rules.

Defining Input and Output:
In FLC error and change in error plays an important role to define controller input. For FLC the inputs are error (E) and change in error (CE).Where E is input is error between the reference speed r and actual speed a. The output for FLC is the change in armature voltage (CU). The equations of input & output are given by equations:
E= e(k )= r (k ) a (k) ——(3)
CE= e(k ) e(k1 )—————(4)
CU= u(k ) u(k1 )————(5)
Block diagram of Fuzzy logic controller including the input and output values is as shown in fig.3.
Fig.3: Block diagram of fuzzy logic controller

Defining membership functions and rules:
Table 2 shows the fuzzy linguistic terms used in this paper.
Table 2: Fuzzy Linguistic Terms
Term
Definition
PB
Positive Big
PM
Positive Medium
PS
Positive Small
ZE
Zero
NS
Negative Small
NM
Negative Medium
NB
Negative Big
Input and output values are defined by seven fuzzy variables, where linguistic terms are used to represent the input and output from numerical and crisp value to linguistic forms. FLC output will be calculated after converting the input and output from crisp value in to linguistic forms. This conversion is done with the help of Fuzzy membership functions.
The fuzzy membership function can be of different shapes such as triangular and trapezoidal.
The FLC uses Fuzzy rules. These rules are in the form of IF_THEN statements If error E is negative big (NB) and change in error (CE) equal to positive big (PB) then change in armature Voltage (CU) is zero (ZE). Table III shows the initial rules.
E CE
NB
NM
NS
ZE
PS
PM
PB
PB
ZE
PS
PM
PB
PB
PB
PB
PM
NS
ZE
PS
PM
PB
PB
PB
PS
NM
NS
ZE
PS
PM
PB
PB
ZE
NB
NM
NS
ZE
PS
PM
PB
NS
NB
NB
NM
NS
ZE
PS
PM
NM
NB
NB
NB
NM
NS
ZE
PS
NB
NB
NB
NB
NB
NM
NS
ZE
Table 3: Initial Rules
With the basic reference of PI control the initial rules are constructed.
The process of defuzzification is required to send out armature voltage. The output in the form of fuzzy sets is converted to crisp value for getting the armature voltage. The center of gravity method is used as defuzzification method.

Adjusting fuzzy range of membership functions and rules:
By adjusting the membership functions the performance of FLC can be improved. When the
the membership function duration is changed , finer control is achieved . The final membership functions are obtained by adjusting membership f unction and rules.
Table 4: Final Rules
220
Fuzzy Logic Controlled Response Reference Input
200
DC Motor Speed Control using Fuzzy Logic Controller
E CE
NB
NM
NS
ZE
PS
PM
PB
PB
NM
NS
NS
NB
PB
PB
PB
PM
NM
NM
NS
NB
PB
PB
PB
PS
NB
NM
NM
ZE
PB
PB
PB
ZE
NB
NB
NM
ZE
PM
PB
PB
NS
NB
NB
NB
ZE
PM
PM
PB
NM
NB
NB
NB
NB
PS
PM
PM
NB
NB
NB
NB
NB
PS
PS
PM
180
Motor Speed rad/sec
160
140
120



Simulation Results
Speed control system of dc motor using FLC is developed by using basic PI controller. The DC motor speed response for PI controller is as shown in Fig.2.When fuzzy logic controller is developed it gives results as shown in Fig.4.The results can be developed by modifying the membership functions ,The modified results are as shown in Fig.5.
DC Motor Speed Control Using Fuzzy Logic Controller
Fuzzy Logic Controlled Response Reference Input
240
220
200
Motor Speed rad/sec
180
160
140
120
100
80
2.5 3 3.5 4 4.5 5
Time in Sec
100
80
2.5 3 3.5 4 4.5 5
Time in Sec
Fig.5: DC motor speed response with modified FLC

Conclusion:
From the results obtained by simulating the Mat lab/Simulink models of PI controller and fuzzy logic controller, It is observed that the fuzzy logic controller with modified membership function outperforms the conventional controller. This gives the scope for research to improve performance by incorporating knowledge and modifying membership functions.
The comparison can be understood with the help of following table.
Table 5: Comparison of Different Controllers
Type of Controller 
Overshoot in rpm 
Settling Time 
PI Controller 
210 rpm 
3.3 sec 
FLC 
222 rpm 
3.25 sec 
FLC with modified membership functions 
201 rpm 
3.2 sec 
FLC
Fig.4: Dc motor speed response with
7. References

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H. Butler, G. Honderd, and J. V. Amerongen, "Model reference adaptive control of a directdrive DC motor," IF.RF. Mag. Cont. Sys., vol. 9, no. 1, pp. 8084, 1989.

H. Ying, W.Siler, and J.JBckley,Fuzzy control theory: nonlinear case,Automatics, vol.26, no3, pp.513 52 0, 1990

J. Klir, George, Yuan, Bo.Fuzzy sets and Fuzzy logic Theory Applications

B. Kosko, Neural networks and fuzzy systems, Prentice hall, 1991.

M. H. Nehrir, F. Fatehi, and V. Gerez,, Computer modeling for enhancing instruction of electric machinery ,IEEE Trans Educ 38 (1995), 166~170.

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