Design A Hybrid Intelligent Controller (Fuzzy-Based Ant Colony Algorithm) For Improving A Tracking Performance Of Actual Output Response

Of SEDC
Motor Under The Effect Of External Disturbances
International J ournal of Electrical, Electronics and Data Communication, ISSN (PRINT): 2320-2084, Volume 1, Issue 2, 2013

1
DESIGN A HYBRID INTELLIGENT CONTROLLER (FUZZY-BASED
ANT COLONY ALGORITHM) FOR IMPROVING A TRACKING
PERFORMANCE OF ACTUAL OUTPUT RESPONSE OF SEDC
MOTOR UNDER THE EFFECT OF EXTERNAL DISTURBANCES

1
AHMAD M.EL-FALLAH ISMAIL,
2
RAJIV RANJAN TEWARI

1
Department of Electrical and Electronic Engineering, University of Allahabad (India)
2
Department of Electronics and Communication, University of Allahabad (India)
Email: Ahmad_engineer21@yahoo.com, rrt_au@rediffmail.com

Abstract: For electrical drives good dynamic performance is mandatory so as to respond to the changes in command speed
and torques, so various speed control techniques are being used for real time applications. The speed of a DC motor can be
controlled using various controllers like PID Controller, Fuzzy Logic Controller, Ant Colony Algorithm (ACA) and Hybrid
Fuzzy-ACA Controller. Fuzzy-ACA Controller is recently getting increasing emphasis in process control applications. The
paper describes application of Hybrid Fuzzy-ACA Controller in an enhancement of stability and accuracy of the SEDC
Motor under the effect of the external disturbances and noise that is implemented in MATLAB/SIMULINK. The simulation
study indicates the superiority Hybrid Fuzzy-ACA Controller over the Ant Colony Algorithm (ACA) and fuzzy logic
controller separately. This control seems to have a lot of promise in the applications of power electronics. The speed of the
SEDC motor can be adjusted to a great extent so as to provide easy control and high performance. There are several
conventional and numeric types of controllers intended for controlling the SEDC motor speed and executing various tasks:
PID Controller, Fuzzy Logic Controller; or the combination between them: Fuzzy-Swarm, Fuzzy-Neural Networks, Fuzzy-
Genetic Algorithm, Fuzzy-Ants Colony, Fuzzy-Particle Swarm Optimization. We describe in this paper the use of Ant
Colony Algorithm (ACA) for designing an optimal fuzzy logic controller of a SEDC Motor. In this case, our approach will
optimize the membership functions of a fuzzy logic controller (FLC) using ACA and the obtained results were simulated on
Matlab environment. Excellent flexibility and adaptability as well as high precision and good robustness are obtained by the
proposed strategy.

Keywords: Separately Excited DC Motor (SEDC), Fuzzy Logic Controller (FLC), Ant Colony Algorithm(ANA).

I. INTRODUCTION

In spite of the development of power electronics
resources, the direct current machine became more
and more useful. Nowadays their uses isnt limited in
the car applications (electrics vehicle), in applications
of weak power using battery system (motor of toy) or
for the electric traction in the multi-machine systems
too. The speed of SEDC motor can be adjusted to a
great extent as to provide controllability easy and
high performance [1,2].We describe in this paper an
approach for designing a fuzzy logic controller of a
SEDC Motor optimized with ACA. Nowadays, to
obtain optimal controllers is a hard task and is
complicated to choose the optimal parameters in the
control system; therefore, in this case, we design an
optimal fuzzy logic controller using an optimization
method, in this case ACA. In the last few years, a
large number of contributions based on techniques
such as neural networks and genetic algorithms have
been proposed to solve this problem. Recently, the
design of FLC has also been tackled with ACO
algorithm and has been successfully applied into the
electrical engineering [3].
The ACO optimization algorithm is a new paradigm
of bio-inspired algorithms that has shown a very good
behavior when solved hard combinatorial
optimization problems as the travelling salesman
problem or the quadratic assignment problem (QAP).
The main advantage of this technique is the global
search guided by heuristic and memoristic
information in a very efficient way that it applies. In
control applications, the fitness is usually related to
performance measures as integral error, setting time,
etc. GA based FLC have been used in induction
motor control system design successfully [4],
Differently from most of previous works [5-9], the
fuzzy rules are optimized by ACO, and the
parameters of the FLC are tuned on-line successfully
in this paper. At last the ACO based FLC has been
applied to the control system of SEDC Motor for
Enhancement of stability and accuracy of the SEDC
Motor under the effect of the external disturbances
and noise. The controller has improved the dynamic
performance and the robustness of the SEDC Motor.
The simulation results have demonstrated that the
performance of the ACO based FLC is better than the
ACA and Fuzzy logic controller separately.

II. MODELING WITHOUT & UNDER THE
EFFECT OF THE LOAD

The S.E.DC motor transfer function without load is
shown in the block diagram in Fig.1.


Design A Hybrid Intelligent Controller (Fuzzy-Based Ant Colony Algorithm) For Improving A Tracking Performance Of Actual Output Response Of SEDC
Motor Under The Effect Of External Disturbances
International J ournal of Electrical, Electronics and Data Communication, ISSN (PRINT): 2320-2084, Volume 1, Issue 2, 2013

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Fig.1 Block diagram of S.E.DC motor without load (Td)

The equations of The SEDC motor in terms of
armature control based on Newtons law combined
with Kerchiefs law are as follows:
(1)
(2)
The motor torque equation is
(3)
Where

At (Td=0) which without disturbance torque
(External Disturbance and Noise), . Where TL
is the load torque, Td is the disturbance torque &Tm
is The motor torque. Using the Laplace transform for
equations (1), (2) and (3) assuming initial conditions
equal zero can be written as



S.E.DC motor transfer function of armature control
from the Applied armature voltage (input voltage)
Va(s)

(10)
S.E.DC motor transfer function with load Td(s) is
shown in the block diagram in Fig.2 [10-11].


Fig. 2 Block diagram of S.E.DC motor with load (Td)

III. FUZZY LOGIC CONTROLLER

The concept of fuzzy logic was developed by Lotfi
Zadeh in 1964 to address uncertainty and imprecision
which widely exist in engineering problems. Fuzzy
modeling is the method of describing the
characteristics of a system using fuzzy inference
rules. The method has a distinguishing feature in that
it can express linguistically complex nonlinear
systems. It is however, very hard to identify the rules
and tune the membership functions of the fuzzy
reasoning. Fuzzy controllers are normally built with
the use of fuzzy rules. These fuzzy rules are obtained
either from domain experts or by observing the
people who are currently doing the control. The
membership functions for the fuzzy sets will be
derived from the information available from the
domain experts and/or observed control actions. The
building of such rules and membership functions
require tuning. That is, performance of the controller
must be measured and the membership functions and
rules adjusted based upon the performance. This
process will be time consuming. The basic
configuration of Fuzzy Logic Controller (FLC)
consists of four main parts (i) Fuzzification where
values of input variables are measured and a scale
mapping that transforms the range of values of input
variables into corresponding universe of discourse is
performed then performs the function of fuzzification
that converts input into suitable linguistic values,
which may be, viewed labels of fuzzy sets. (ii)
Knowledge Base consists of data base and linguistic
control rule base. The database provides necessary
definitions, which are used to define linguistic control
rules and fuzzy data, manipulation in an FLC. The
rule base characterizes the control goals and control
policy of the domain experts by means of set of
Design A Hybrid Intelligent Controller (Fuzzy-Based Ant Colony Algorithm) For Improving A Tracking Performance Of Actual Output Response Of SEDC
Motor Under The Effect Of External Disturbances
International J ournal of Electrical, Electronics and Data Communication, ISSN (PRINT): 2320-2084, Volume 1, Issue 2, 2013

3
linguistic control rules. (iii) The Decision Making
Logic, it has the capability of simulating human
decision making based on fuzzy concepts and of
inferring fuzzy control actions employing fuzzy
implication and the rules of inference in fuzzy logic.
(iv) The Defuzzification a scale mapping which
converts the range of values of input variables into
corresponding universe of discourse [12-16]. In view
to make the controller insensitive to system
parameters change, fuzzy logic theory is also
implemented by researchers extensively. Indulkar et.
Al [17] initially designed a controller using fuzzy
logic for automatic generation control and responses
were compared with classical integral controller.
Chang et. al. [18] presented a new approach to study
the LFC problem using fuzzy gain scheduling of
proportional integral controllers and proposed scheme
has been designed for a four area interconnected
power system with control deadbands and generation
rate constraints. Ha [19] applied the robust sliding
mode technique to LFC problem where, control
signal consists of an equivalent control, a switching
control and fuzzy control with generation rate
constraints and governors backlash on the other hand
the fuzzy controller designed by Chown et. al [20]
when implemented not only grid was controlled
better but also more economically. Talaq et. al [21] in
their research proposed an adaptive controller which
requires less training patterns as compared with a
neural net based adaptive scheme and performance
was observed better than fixed gain controller. Ha et.
al [22] proposed an approach which combines the
salient features of both variable structure and fuzzy
systems to achieve high performance and robustness.
Fuzzy logic controller, designed by El-Sherbiny [23],
is a two layered fuzzy controller with less overshoot
and small settling time as compared with
conventional one. Ghoshal [24] presented a self
adjusting, fast acting fuzzy gain scheduling scheme
for conventional integral gain automatic generation
controller for a radial and ring connected three equal
power system areas. Yensil et. Al [25] proposed a self
tuning fuzzy PID type controller for LFC problem
and satisfactory results are found when compared
with fuzzy PID type controller without self tuning.

IV. ANT COLONY ALGORITHM

Ant Colony Algorithm is inspired by the real ant
colonys behaviour. Its a new imitate biologic
algorithm and has been generated for more than ten
years which is the observational result from the
nature.
In the animate nature ants have the ability to find out
the food from the nest in the shortest path without
any visible reminder. The core of ant colony
algorithm is to dispose the pheromone leaving by ants
and to find the optimal path, ants release the kind of
pheromone in the path and make other ants perceive
it in a certain range even influence their behavior.
The more ants pass the path, the more pheromone
will accumulate, so the subsequent ants have more
probability to select those paths, which have more
pheromone, then its a positive feedback. Ant
algorithm has been successfully used to solve many
NP problems, such as TSP, assignment problem, job-
shop scheduling and graph coloring. But it often
brings two problems; one is the search easier runs
into the local optimum when solving the problem, i.e.
all individual find a complete accordance solution
after the search proceed to determinate degree. That
is deadlock occurs and further search cant carry on
in solution space. It is possible result in global
optimum solution cant be found. The other is it need
long time to converge at the global optimum. Solving
result oscillates between local optimum solution and
global optimum solution [26]. A way ants exploit
pheromone to find a shortest path between two points
is shown in Fig. 3.

Fig. 3 How real ants find a shortest path

V. HYBRID FUZZY ACA CONTROLLER

The methodology of this paper involved the
Development of a Ant Colony Algorithm (ACA) to
optimize the parameters of membership functions of a
fuzzy logic controller that uses for the enhancement
of stability and accuracy of the SEDC Motor under
the effect of the external disturbances and noise. This
is possible evaluating the ranges of membership
functions within the fuzzy logic controller. After
getting the optimal parameter the full model was
implemented in Matlab environment where the ACA
will create the optimal topologies to achieve a
possible solution to this problem, This paper applies
the ACO based fuzzy controller to the SEDC Motor.
The fuzzy rules are optimized off line, while the
parameters of the fuzzy controller are tuned on line.
By comparison with the Fuzzy logic controller and
the ACA separately, the hybrid Fuzzy-ACA
controller, it is not only more robust, but can also
achieve a better static and dynamic performance of
the system.

Design A Hybrid Intelligent Controller (Fuzzy-Based Ant Colony Algorithm) For Improving A Tracking Performance Of Actual Output Response Of SEDC
Motor Under The Effect Of External Disturbances
International J ournal of Electrical, Electronics and Data Communication, ISSN (PRINT): 2320-2084, Volume 1, Issue 2, 2013

4
VI. DESIGN REQUIREMENTS FOR THE
SYSTEM

The most basic requirement of S.E.DC motor is that it
should be rotated at the desired speed without and
under the effect of loads (external disturbances and
noise) and intelligent controller is used for reducing
the sensitivity of actual response as to load variations
(external disturbances and noise), where the actual
response variations that have been induced by such
external disturbances and noise must be minimized
rapidly. The steady-state error of the S.E.DC motor
speed should be minimized. The other performance
requirement is that motor must accelerate to its
steadystate speed as soon as it turns on, The SEDC
motor is driven by applied voltage. The reference
input (applied voltage) (V) is simulated by unit step
input, then an actual response of S.E.DC motor
should have the design requirements for the system as
follows

(i) Minimize the maximum overshoot
(ii) Minimize the rise time
(iii) Minimize speed tracking error
(iv) Minimize the steady state error
(v) Minimize the settling time
(vi) The system is controllable and observable
(vii)All roots of characteristic equation are lying in
the left half of s-plane.
(viii) Damping ratio () is between (0.4 & 0.86).

The speed of a SEDC motor could be varied from
zero to rated speed mainly by varying armature
voltage in the constant torque region. Whereas in the
constant power region, field flux should be reduced to
achieve speed above the rated speed. The motor
drives a mechanical load characterized by inertia J,
Viscous friction coefficient B, and load torque TL.
The specifications of the SEDC motor are given in
table 1.

Table 1. The specifications of the SEDC motor

VII. SIMULATION RESULTS

Fig.4 shows The structure of the fuzzy controller with
ACA algorithms and Figs. 5-7 show results of
simulation (Matlab environment) of a Enhancement
of stability and accuracy of the SEDC Motor without
and under the effect of the external disturbances and
noise by intelligent controller (Fuzzy logic controller,
Ant Colony algorithm (ACA), FLC+ACA ). The
actual response of FLC+ACA Controller comparing
with the actual response of FLC, and PSO algorithms
is shown in Fig. 6. Table 2 lists the Comparison of
the performances of Fuzzy, ACA and hybrid Fuzzy-
ACA controllers, to show the effectiveness of the
proposed approach.

Fig. 4 the structure of the Fuzzy controller with ACA

CONCLUSION

This paper applies the ACO based fuzzy controller to
the SEDC Motor. The fuzzy rules are optimized off
line, while the parameters of the fuzzy controller are
tuned on line. By a comparison the Hybrid Fuzzy-
ACA Controller, ACA and Fuzzy logic controller, the
Hybrid Fuzzy-ACA Controller is not only more
robust, but can also achieve a better static and
dynamic performance of the system. The output
response confirms the suitability of the proposed
methodology as a high-performance control system
for the SEDC Motor.. By using Hybrid Fuzzy-ACA
Controller for enhancement of stability and accuracy
of the SEDC Motor under the effect of the external
disturbances and noise, . The speed response for
constant load torque shows the ability of the drive to
instantaneously reject the perturbation. The design of
controller is highly simplified by using a cascade
structure for independent control of flux and torque.
Excellent results added to the simplicity of the drive
system, makes the Hybrid Fuzzy-ACA Controller
based control strategy suitable for a vast number of
industrial, paper mills etc. The sharpness of the speed
output with minimum overshoot defines the precision
of the proposed drive. Hence the simulation study
indicates the superiority of Hybrid Fuzzy-ACA
Controller over the Ant Colony Algorithm (ACA)
and Fuzzy logic controller separately. . This control
seems to have a lot of promise in the applications of
power electronics. After having applied the proposed
FLC-ACA method we can conclude in this paper, that
the use of optimized Fuzzy Logic Controllers is
possible to achieve very good results. In particular
with this application we are demonstrating
statistically that there is significant difference when
the controllers are developed manually or
automatically. Therefore, with the results presented in
the paper we can recommend the use of optimization
methods to find some important parameters, in this
case, ACA was only used to design the optimal
topology of the membership functions.
Design A Hybrid Intelligent Controller (Fuzzy-Based Ant Colony Algorithm) For Improving A Tracking Performance Of Actual Output Response Of SEDC
Motor Under The Effect Of External Disturbances
International J ournal of Electrical, Electronics and Data Communication, ISSN (PRINT): 2320-2084, Volume 1, Issue 2, 2013

5

Fig 5 Step response of the system with external load at time
3sec and without adding controller.


Fig.6 Simulation Results of the comparison among the
Fuzzy,ACA and Hybrid Fuzzy-ACA Controller.


Fig. 7 the behavior of Ant Colony Algorithm (ACA)

Table 2 Comparison of Fuzzy, ACA and hybrid Fuzzy-ACA
controller.
REFERENCES

[1] Hnao H., Capolino G. A. Mthodologie et application du
diagnostic pour les systems lectriques. Article invit dans
Revue de l'Electricit et de l'Electronique (REE), (Text in
French) 2002, 6, p. 79- 86.
[2] Raghavan S. Digital control for speed and position of a DC
motor. MS Thesis, Texas A&M University, Kingsville,
2005.
[3] J Casillas, O Cordon, and F Herrera, Learning fuzzy rules
using ant colony optimization algorithms, Proceedings of
the 2nd International Workshop on Ant Algorithms,
Brussels, Belgium, p.13-21, 2000.
[4] Won-Seok Oh, Young-Tae Kim, Chang-Sun Kim, Tae-Seok
Kown, and Hee-J un Kim, Speed control of induction motor
using genetic algorithm based fuzzy controller, Industrial
Electronics Society Conference of IEEE, USA, vol. 2, p.
625-629, 1999.
[5] DA Silva, W Acrnley, and Finch, Application of genetic
algorithm to the online tuning of electric drive speed
controllers, IEEE Transactions on Industrial Electronics,
vol. 47, no. 1, pp.217-219, February 2000.
[6] J ulu Sun, Pengsheng Su, Yongdong Li, and Lichun Li,
Application of self- adjusting fuzzy controller in a vector-
controlled induction motor drive, Power Electronics and
Motion Control Conference, China, vol. 8, p. 1197-1201,
2000.
[7] Changliang Xia, Peijian Guo, Tingna Shi, and Mingchao
Wang, Speed control of brushless DC motor using genetic
algorithm based fuzzy controller, Proceedings. 2004
International Conference on Intelligent Mechatronics and
Automation, China, p. 460-464, 2004.
[8] Changliang Xia, Peijian Guo, Tingna Shi, and Mingchao
Wang, Speed control of brushless DC motor using genetic
algorithmbased fuzzy controller, Proceedings of the CSEE,
vol. 25, no.11, pp.129-133, J une 2005.
[9] Ahmed Rubaai, and M. David Kankam, Experimental
verification of a hybrid fuzzy control strategy for a high-
performance brushless DC drive system, IEEE
TRANSACTIONS ON INDUSTRY APPLICATIONS,
vol.37, no.2, pp.503-512, March 2001.
[10] Brahim GASBAOUI and Brahim MEBARKI (2009)
Setting Up PID DC Motor Speed Control Alteration
Parameters Using Particle Swarm Optimization Strategy
Leonardo Electronic J ournal of Practices and Technologies,
ISSN 1583-1078, Issue 14, J anuary-J une 2009, p. 19-32.
[11] Mehdi Nasri, Hossein Nezamabadi-pour, and Malihe
Maghfoori (2007) A PSO-Based OptimumDesign of PID
Controller for a Linear Brushless DC Motor World
Academy of Science, Engineering and Technology 26 2007,
Manuscript received J an. 17, 2007.
[12] Z.A. Zadeh, Fuzzy sets, Information and Control, vol. 8,
1965, pp. 338-353.
[13] S.K. Pal et al., Fuzzy logic and approximate reasoning: An
Overview, J ournal of Institution of Electronics and
Telecommunication Engineers, Paper No. 186-C,1991, pp.
548- 559.
[14] Y.H. Song and A.T. J ohns, Application of fuzzy logic in
power systems: Part 1 General Introduction to fuzzy logic,
IEE power Engineering J ournal, Vol. 11, No. 5, 1997, pp.
219-222.
[15] Y.H. Song and A.T. J ohns, Application of fuzzy logic in
power systems: Part 2 Comparison and Integration with
expert systems, neural networks, and genetic algorithms,
IEE power Engineering J ournal, Vol. 12, No. 4, 1998, pp.
185-190
[16] J Nanda and B L Kaul, Automatic Generation Control of
An Interconnected Power System, IEE Proc, vol 125, no 5,
May 1978, p 384.
[17] C.S. Indulkar and Baldev Raj, Application of fuzzy
controller to automatic generation control, Electric
machines and power system, vol. 23, No. 2,1995, pp. 209-
220.
[18] C.S. Chang and W. Fu, Area load frequency control using
fuzzy gain scheduling of PI controllers, Electric power
systemresearch, Vol. 42,1997, pp. 145-152.
[19] Q.P.Ha, A fuzzy sliding mode controller for power sytem
load frequency control in Proc. 1998 second international
conference on knowledge based intelligent electronic
systems, 21-23 April, pp. 149-154.
Design A Hybrid Intelligent Controller (Fuzzy-Based Ant Colony Algorithm) For Improving A Tracking Performance Of Actual Output Response Of SEDC
Motor Under The Effect Of External Disturbances
International J ournal of Electrical, Electronics and Data Communication, ISSN (PRINT): 2320-2084, Volume 1, Issue 2, 2013

6
[20] J awad Talaq and Fadel Al-Basri, Adaptive fuzzy gain
scheduling for load frequency control IEEE Transactions
on power system, vol. 14, No. 1 February 1999, pp. 145-150
[21] Q.P. Ha and H. Trinh,A variable structure based ontroller
with fuzzy tuning for load frequency control International
J ournal of power and energy systems, vol. 20 No. 3, 2000,
pp. 146-154.
[22] M.K. El-Sherbiny, G. El-Saady, Ali M. Yousef, Efficient
fuzzy logic load frequency controller Energy Conversion
and Management, 43, 2002, pp. 1853-1863.
































































[23] S.P. Ghoshal , Multi area frequency and tie line power
flow control with fuzzy logic based integral gain
scheduling J ournal of Institute of Engineers, vol. 84,
December 2003, pp. 135-141
[24] E. Yesil, M. Guzelkaya, I. Eksin, Self tuning fuzzy PID
type load and frequency controller Energy Conversion and
Management, 45, 2004, pp. 377-390.
[25] Yong-Feng Dong, J un-Hua Gu, Na-Na li, Xiang-Dan Hou,
Wei-Li Yan , "Combination of genetic algorithmand ant
colony algorithm for distribution network planning ".
Proceedings of the Sixth International Conference on
Machine Learning and Cybernetics, Hong Kong, 19-22
August 2007 1-4244-0973-X/07/$25.00 2007 IEEE.