Speed Conrol of Separately Excited dc Motor using Fuzzy Technique

ACEEE Int. J. on Electrical and Power Engineering, Vol. 02, No. 03, Nov 2011

Speed Conrol of Separately Excited dc Motor using
Fuzzy Technique
Ram kumar karsh1, Dr. G.K.Choudhary2, Chitranjan Kumar3
1
Dept. of Electrical Engineering, NIT Patna, India
1
tnramkarsh@gmail.com
2
H.O.D, Dept. of Electrical Engineering, NIT Patna,India
(girishkrchoudhary, chitranjan_kumar24)@yahoo.co.in

Abstract- This paper presents the speed control of a separately
excited DC motor using Fuzzy Logic Control (FLC). The Fuzzy d m
Logic Controller designed in this study applies the required J m
dt
 K n Ia - bm  M load (1)
control voltage based on motor speed error (e) and its change
(ce). The performance of the driver system was evaluated dIa
through digital simulations using Simulink. The simulation La  Va -I a R a  K b Ø m (2)
results show that the control with FLC outperforms PI control dt
in terms of overshoot, steady state error and rise time. The dynamic model of the system is formed using these
Keywords: DC motor, chopper, FLC. differential equations and Matlab Simulink blocks as shown
in Fig. I,
I. INTRODUCTION
DC motors are used in many applications like electric
trains, vehicles, cranes and robotics manipulators. They
require controlling of speed to perform their tasks. Initially
speed control of DC motor has been done by voltage control
[1]. Semiconductors too like MOSFET, IGBT and GTO have
been used as switching devices to control speed[2].
Due to nonlinearity properties, control of system is
difficult and mathematically tedious. To overcome this
difficulty, FLC (Fuzzy Logic control) has been developed.
FLC is applicable to time variant and nonlinear. Metro system
in the sendia of japan is the best application [3].
In this study, the speed response of a separately excited
DC motor exposed to fixed armature voltage is studied for
both loaded and unloaded operating conditions. Performance
of separately excited DC motor is compared for both methods
FLC and PI controller for both loaded and unloaded
conditions. In this study, chopper circuit has been used as a
motor driver.

II. MOTOR MODEL
Fig 1: Simulink Motor model
The resistance of the field winding and its inductance are
represented by Rf and Lf respectively. The armature, resistance TABLE I. MOTOR PARAMETERS
and inductance are represented by Ra and La respectively.
Armature reactions effects are ignored in the description of
the separately excited DC motor. This negligence is justifiable
to minimize the effects of armature reaction since the motor
(SE) used has either interpoles or compensating winding.
The fixed voltage Vf is applied to the field and the field current
settles down to a constant value. A linear model of a simple
separately excited DC motor consists of a mechanical equation
and electrical equation as determined in the following
equations:

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DOI: 01.IJEPE.02.03. 533


III. FUZZY LOGIC CONTROLLER (FLC) DESCRIPTION AND DESIGN
Fuzzy logic control is based on logical relationships like
“suitable, not very suitable, high, little high, much and far
too much that are frequently used words in people’s life.
Fuzzy Sets Theory has been introduced to express and process
fuzzy knowledge [4], [5] which are used to show linguistic
variables. The relation between fuzzy logic and fuzzy set
theory that is similar that of relation between Boolean logic
and set theory. Fig. 2 shows a basic FLC structure.

Fig. 4. Change of Error

Fig. 2 Process Blocks for a Fuzzy Controller
FLC is processed for linguistic definitions, while other
contrllers work on the accuracy and parameters of system
model. While designing FLC, there is no need of knowledge
of system model, as a controller. However, less knowledge of
control process may result unsatisfactory [6].
Fig. 5. Dynamic Signal Analysis
A. Defining inputs, outputs:
B. Defining membership functions and rules:
As the bigger speed error the bigger controller input is
expected. So, FLC is designed to minimize speed error. Due to System speed comes to reference value by means of the
that FLC uses error (e) and change of error (ce) for linguistic defined rules. For example, first rule on Table determines, ‘if
variables which are generated from the control rules. Control (e) is PL and (ce) is PL than (c.) is NL’ According to this rule,
variable (cu) is applied to achieve angular value (alpha), which if error value is positive large and change of error value is
determines duty cycle. positive large than output, change of alpha will be negative
large. In this condition, corresponding A4 interval in Fig 5,
e(k) = [ ωr(k) - ωa(k) ] * K1E motor speed is smaller than reference speed and still wants
to decrease strongly. This is one of the worst conditions in
ce(k) = [ e(k) - e(k-1) ] * K2CE (3)
control process. Because of the fact that alpha is smaller
cα(k) = [ α(k) - α(k-1) ] * K3cα
than the required value, its value can be increased by giving
Here K1E, K2CE and K3cα are each gain coefficients and K is a output PL value. This state corresponds to motor voltage
time index. decreasing. All conditions in control process are shown in
Fig.5. Membership functions have been used to convert
inputs and outputs from crisp value to linguistic term.
Linguistic terms are represented here by seven membership
functions shown in table.

Fig. 3. Block diagram of the DC motor control
At nominal value of motor speed the error (e) gives its smallest
value, and at maximum value of motor speed the error gives
its larger value, with range –200 and 200.
Fig. 6. Linguistic rules for angle (alpha) determination for driver
circuit. It will for a) speed error. b) Change in speed error. c)
Change of alpha
© 2011 ACEEE 32
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TABLE II. T HE RULE D ATA BASE

IV. DRIVER CIRCUIT AND MODELING
DC chopper has been used to drive the motor also changes
average value of load voltage applied from a fixed DC source
by switching a power switch.

Fig 9. General waveform for current continuous condition

Fig 7. Operating principle and output waveform of Driver
Using Fig 7, the average output voltage can be calculated as

V do  t on
V (4)
t on t off


Where V is the DC source voltage. vdo can be controlled
Fig. 10. DC Chopper model
using three methods:
*Hold toff fixed and change ton (frequency modulation) Fundamentally, the operating principle of driver model is
*Hold period (ton + toff) fixed and change toff /ton rate (pulse based on the comparison of two signals [7]. One of the sig-
width modulation) nals is a triangular waveform which represents one PWM is
*change toff and ton separately. (Combination of first and used to control average output voltage period of 2 KHz chop-
second method) ping frequency and other one is fixed linear signal which
One–quadrant DC chopper and general waveforms for represents time equivalent of alpha triggering (ta). Since
continuous current conditions are shown in fig.8 chopping frequency is 2 KHz, the amplitude of triangular
waveform starts from zero and reaches 1 / 2.103 = 0.0005 value.
On the other hand, the alpha signal from controller is multi-
plied by 0.0005/360 value to calculate the time corresponding
to this angle. Alpha signal and triangular signal are U1 and
U2 variables of ‘1F’ block used in simulation model shown in
Fig. 10, respectively.

Fig 8. simple power circuit of a one quadrant DC chopper

Fig 11. input and output signal of driver model

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V. CONTROL SIMULATION lation results are shown for 50 nm load applied at 0.6s. Simu-
lation result for PI controller for loaded and no loaded condi-
In FLC model gain1, gain2 and gain3 define change of error, tion is shown in table iii
error and change of alpha scaling factors respectively. Simu

Fig 12. Fuzzy Logic Controller Simulink model

(b) Unloaded operation

Percent overshoot (%Mp) and steady state error (ess) are
measured for different load.
Fig 13. a) Speed response of PI controller for used motor
CONCLUSION
Fuzzy Logic Controllers are a suitable option to make
speed regulation in DC motors and AC motors. The quality of
the control obtained with FLC’s at the first tries is commonly
good because is based on the knowledge of an expert. It can
not be said the same about conventional controllers. The
single human based reasoning used on a FLC can be very
useful to overcome nonlinearities of any kind of plants in a
logical way. The experience gained from these works has
Fig 13. b) Speed response of Fuzzy controller for used motor allowed us to attack another systems of very different nature
TABLE III PERFORMANCE ANALYSIS OF SYSTEM obtaining satisfactory results. Comparison between PI
controller responses and FLC responses is shown in table iii
C1: Kp=100, Ki=15 Tr: Rise time and shows that FLC gives better performance than PI
C2: Kp=200,Ki=25 ess : Steady state error controller in terms of overshoot , steady state error and rise
time. Also show that FLC is more sensitive to load changes.
C3: Kp=300, Ki=28 %Mp: Percentage Maximum overshoot It would be necessary to use a more complex intelligent control
C4: Kp=500, Ki=30 C1, C2……..Different Kp and Ki system, i.e. Adaptive Fuzzy System, Neuro-Fuzzy System, in
coefficients order to obtain a better performance on speed control
(a) For different loads
REFRENCES
[I] Chan, C. C., Low Cost Electronic Controlled Variable Speed
Reluctance Motors, IEEE Transactions on Industrial Electronics,
Vol. IE-34, No. I. 95-100. February 1987.
[2] Khoei, A.. Hadidi, Kh., Microprocessor Based Closed-Loop
Speed Control System for DC Motor Using Power Mosfet.
Electronics Circuits and Systems IEEE international Conference
ICECS’96, Vol. 2, 1247-1250, 1996.
[3] C. Elmas, “Fuzzy Logic Controllers”, Seqkin Publishing, April-
2003
[4] L. A. Zadeh, ‘‘ Fuzzy Sets” Informal Control, ~01.8p, p 338-
353, 1965.

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[5] L. A. Zadeh, ‘. Outline of a new approach to the analysis Prof. Girish Kumar Choudhary was born
complex systems and decision processes” IEEE Trans. Syst. Man in August, 1959 in a small village. He did his
Cybem, vol. SMC-3, pp. 2844, I973 matriculation in 1975, and was enrolled for
[6] Y. Tipsuwan, Y. Chow, “Fuzzy Logic Microcontroller diploma in Electrical Engineering in the same
Implementation for DC Motor Speed Control”. IEEE. 1999. year. He completed his diploma in Electrical
[7] F. Rahman, .‘Lectures 18 Control of E€-DC Conveners”, Power Engineering securing first position in the
Electronics. ELEC424019240. state of Bihar in 1979. He got his B.Sc
Ram kumar Karsh was born in 1986 in (Engineering) degree in Electrical in 1985 securing first class first
a small village of Janjgir-champa district from Patna University, Patna with distinction. He acquired his
of Chattisgarh. He received B.E. (Electron- Ph.D degree in 1998 from Patna University. He joined BCE Patna
ics & Tele communication) in 2009 with (Presently NIT Patna) in Jan. 1988 as Lecturer in Electrical
first class from Govt Engineering College, Engineering and promoted as Associate Professor Electrical
Bilaspur, India. He is pursuing M.Tech. Engineering in 1996. Subsequently he became Professor in 2006.
degree with specialization Control Sys- Presently he is working as Professor & Head of Electrical
tems from Department of Electrical Engi Engineering at NIT Patna. He is also holding the post of Chairman,
HMC, NIT Patna. He has many publications in National and
neering, National Institute of Technology (NITP), Patna, India.
International Journals and Conferences. He has also achieved the
His field of interest includes fuzzy logic, control systems.
distinction of getting his research product “Adapters for Laptops
and others Electronic Devices.” Patented (No. 235642 dated
10.07.2009). Prior to joining NIT Patna he has also worked in All
India Radio (AIR) Patna and Videsh Sanchar Nigam Limited, Arvi,
Pune.
Chitranjan kumar received B.E. (Elec-
tronics & Tele communication) in 2009
with first class from D.K.T.E.S Ichalkaranji
,Kolhapur, India. He completed M.Tech.
degree with specialization Control Systems
in 2011 with first class from Department
of Electrical Engineering from National In
stitute of Technology (NITP), Patna, India. His field of interest
includes fuzzy logic, signal processing, control systems

© 2011 ACEEE 35
DOI: 01.IJEPE.02.03. 533

Speed Conrol of Separately Excited dc Motor using Fuzzy Technique

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Speed Conrol of Separately Excited dc Motor using Fuzzy Technique