a low-cost four-quadrant chopper-fed embedded dc drive ... · motor drives [2]. fuzzy logic has...
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A Low-cost Four-quadrant Chopper-fed Embedded DCDrive Using Fuzzy ControllerN. Senthil Kumar a , V. Sadasivam b & M. Muruganandam aa Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College,Sivakasi, Tamilnadu, Indiab Department of Computer Science and Engineering, Manonmaniam Sundaranar University,Tirunleveli, Tamilnadu, IndiaVersion of record first published: 31 May 2007.
To cite this article: N. Senthil Kumar , V. Sadasivam & M. Muruganandam (2007): A Low-cost Four-quadrant Chopper-fedEmbedded DC Drive Using Fuzzy Controller, Electric Power Components and Systems, 35:8, 907-920
To link to this article: http://dx.doi.org/10.1080/15325000701199388
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Electric Power Components and Systems, 35:907–920, 2007
Copyright © Taylor & Francis Group, LLC
ISSN: 1532-5008 print/1532-5016 online
DOI: 10.1080/15325000701199388
A Low-cost Four-quadrant Chopper-fed Embedded
DC Drive Using Fuzzy Controller
N. SENTHIL KUMAR
Department of Electrical and Electronics Engineering
Mepco Schlenk Engineering College
Sivakasi, Tamilnadu, India
V. SADASIVAM
Department of Computer Science and Engineering
Manonmaniam Sundaranar University
Tirunleveli, Tamilnadu, India
M. MURUGANANDAM
Department of Electrical and Electronics Engineering
Mepco Schlenk Engineering College
Sivakasi, Tamilnadu, India
Abstract A low-cost fuzzy controller for closed loop control of DC drive fed by
four-quadrant chopper is designed and presented in this article. The fuzzy controlleris implemented in a low-cost 8051 micro-controller based embedded system. The
controller is used to change the duty cycle of the converter; thereby, the voltage fedto the armature of the separately excited motor to regulate the speed. The simulated
closed loop performance of the fuzzy controller in respect of load variation andreference speed change has been reported. Further, the dynamic response of DC
motor with fuzzy controller is tested and found to be satisfactory. As the design ofproposed controller does not depend on any of the motor parameters, it can be used
to control DC drive of any rating by very minor modification in the hardware. Thisadvantage of the proposed system is tested for two different motor parameters.
Keywords DC-DC power conversion, DC motor drives, fuzzy control, micro-controllers
1. Introduction
DC motor drives are highly controllable and are used in many applications such as
Lift, Crane, Robotic manipulators, Traction, etc. High performance servo applications
require the motor drive to follow speed commands with minimal steady state error and
Received 18 April 2006; accepted 3 January 2007.Address correspondence to Prof. N. Kumar, Dept. of Electrical and Electronics Engineer-
ing, Mepco Engineering College Post, Sivakasi, Virudhungar, Tamilnadu, 626 005, India. E-mail:[email protected]
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overshoots. Such drive systems must have fast acceleration and deceleration capabilities
so that the desired speed command profile is always followed, even under load torque
disturbances.
Different control strategies have been implemented to regulate the DC-DC converter
and the DC motors including PI, fuzzy logic control, and sliding mode control. Though
general PI and PID controllers are widely used for motor control applications, their
design depends upon motor parameters. So it does not give satisfactory results when
motor parameters, loading conditions, and the motor itself are changed. The fuzzy logic
controller (FLC) can be designed without the exact model of the system [1]. For FLC,
it is sufficient to understand the general behavior of the system. This approach of FLC
design guarantees the robust control even if there is a change in the parameters on the
motor drives [2].
Fuzzy logic has been implemented for motor control applications using different
converters and micro-controllers, and to regulate the output voltage of a DC-to-DC con-
verter [3]. The design of fuzzy controller for a three-phase full wave controlled converter
fed DC motor is implemented in a 16-bit micro-controller [4]. The fuzzy controller for
a DC motor has been simulated in MATLAB environment and proved to outperform PI
control [5]. Fuzzy controller for four-quadrant DC drives without speed sensor [6] and
a micro-controller based fuzzy controller has been designed and implemented for a DC
motor [7].
In this article, the four-quadrant chopper (Class E chopper) controlled by a fuzzy
controller suitable for embedded system implementation is presented. The four-quadrant
converter is designed to have a switching frequency of 14 KHz. The use of high switch-
ing frequency reduces torque ripples on the motor shaft. Such converter requires both
current and voltage reversing capability to match rapidly changing speed references and
compensate for step load disturbances. Two control loops, such as inner ON/OFF current
controller and outer fuzzy controller, are used in the proposed controller. The quadrant
of operation is decided by comparing the reference speed and the actual speed of the
motor.
This article is organized as follows. Section 2 describes the proposed system of
separately excited motor control. Modeling and simulation of DC motor and the four-
quadrant chopper are discussed in Section 3. Section 4 explains the structure of the fuzzy
controller used and its components. The simulation results of the proposed system are
given in Section 5. The embedded system implementation of the proposed controller is
described in Section 6. Section 7 discusses the conclusions made out of the present work.
2. Proposed System
The block diagram of the proposed system is shown in Figure 1. The system consists
of four-quadrant chopper type DC-to-DC converter for driving the separately excited DC
motor. An 8051 based micro-controller with an inbuilt pulse width modulation (PWM)
unit is used to generate the PWM waveform required to switch the DC-to-DC converter.
A tacho generator is used to sense the speed of the motor.
The designed closed loop control has two loops. One is outer speed control loop
and the other one is inner current control loop. The inner current control uses ON/OFF
control and switches off the PWM signal whenever the motor current exceeds the rated
reference current, ILref . This has the advantage of using DC motor with any specification.
The change in ILref can be easily done by hardware using a potentiometer connected to
the comparator unit.
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Chopper Drive Using Fuzzy Controller 909
Figure 1. Block diagram of the proposed system.
In outer speed control loop, the motor speed sensed by the tacho generator is fed
to an analog to digital converter (ADC), which is an inbuilt unit of microcontroller
system. The motor speed is compared with the reference speed. After comparison, error
signal and the change in error are calculated and are given as input to fuzzy controller.
The fuzzy controller attempts to reduce the error to zero by changing the duty cycle
of switching signal. Fuzzy controller used has two inputs and one output, as shown in
Figure 2. The inputs to the fuzzy controller are error and change in error. The output of
the fuzzy controller is the change in duty cycle. The new duty cycle for the converter is
then calculated from the previous duty cycle and the output of the fuzzy controller.
The PWM signal is generated by the microcontroller using the duty cycle calculated.
This PWM signal is applied to a gating circuit. This gate control switches off the PWM
signal, if the output from the comparator is zero. The comparator compares the actual
motor current with the reference current and switches off the PWM signal if the motor
current exceeds the reference current. The output of the gate control switch is now given
to a selector logic, which can select the switches to be controlled based on the quadrant
of operation. The isolator and driver circuit drive the MOSFET switches using the signals
from the selector unit.
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Figure 2. Structure of the fuzzy controller used.
The model of the DC motor and the DC-to-DC converter was developed and simu-
lated using MATLAB simulink toolbox. The fuzzy controller was designed and simulated
by using the MATLAB fuzzy logic toolbox.
3. Modeling and Simulation of DC Motor and Four-quadrant Chopper
3.1. DC Motor
The simulation of the entire set-up was done based on equation model of the motor. The
DC motor has been modeled using Eqs. (1) and (2).
d 2�
dt2D
1
J
�
KT io � Bd�
dt� TL
�
(1)
dio
dtD
1
L
�
�Rio C Vo � Kb
d�
dt
�
(2)
where
J —moment of inertia of the motor,
B—friction coefficient of the motor,
Kt —torque constant of the motor,
Kb—motor back emf constant,
TL—load torque applied,
io—armature current,
Vo—armature voltage applied,
R—armature resistance, and
L—armature inductance.
The simulated model of the DC motor is shown in Figure 3.
3.2. Four-quadrant Chopper
The Class E four-quadrant chopper can be operated in any one of the four quadrants,
as shown in Figure 4. For the first quadrant of operation, the load voltage and current
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Figure 3. Simulink model for the motor.
are positive. So the power is transferred from source to load. In the second quadrant
of operation, the load current is negative and load voltage is positive. The power flows
in reverse direction from load to supply side. In the third quadrant operation, the load
voltage and current are negative. Power flows from DC source to load. For the fourth
quadrant operation, the load voltage is negative and the load current is positive. The
power flows from load to supply.
Thus, the operation of the chopper is used to drive DC motors with forward motoring
in first quadrant, forward braking in second quadrant, reverse motoring in third quadrant,
and reverse braking in fourth quadrant [8, 9].
The chopper output voltage and current during different quadrants of operation are
given in Table 1. Every quadrant of operation will have two modes of conduction switches
and they are also given in the table. The converter output voltage and current were
simulated using proper switching signals applied to the chopper switches.
Figure 4. Four-quadrant chopper.
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Table 1
Chopper switching and quadrant of operation
Conducting switchesChopper output
voltage in
Operating mode
Mode 1
(motoring
or braking)
Mode 2
(free-
wheeling) Mode 1 Mode 2
Chopper
output
current
Forward motoring (I quadrant) Q1, Q4 D2, Q4 Vs 0 Positive
Forward braking (II quadrant) D1, D4 Q2, D4 0 Vs Negative
Reverse motoring (III quadrant) Q3, Q2 D4, Q2 �Vs 0 Negative
Reverse braking (IV quadrant) D3, D2 Q4, D2 0 �Vs Positive
4. Design of Fuzzy Controller
4.1. Fuzzy Logic Control
Fuzzy logic controller is an attractive choice when precise mathematical formulations
are not possible [10]. Fuzzy logic controllers are more robust than other non-linear
controllers and do not need fast processors. Further, they can work with less storage than
the conventional look up table for non-linear controllers.
Fuzzy logic is used in an outer speed control loop. The speed is fed back and
is compared with the reference speed. After comparison, the error and the change in
error are calculated and are given as input to fuzzy controller. In this work, the error
is normalized to per unit value with respect to the reference speed. This helps in using
the fuzzy controller for any reference speed. The fuzzy controller will attempt to reduce
the error to zero by changing duty cycle of switching signal. The general PI, like fuzzy
controller shown in Figure 2, is used in this work [8].
4.2. Sugeno Fuzzy Controller
There are two types of fuzzy controllers, viz., Mamdani and Sugeno type fuzzy con-
trollers. In this work, Sugeno fuzzy controller is used. It uses singleton membership
functions for the output variables. The Sugeno type controller is used because it can be
easily implemented in any embedded system and reduces calculations. Furthermore, the
reduction in calculation can result in real-time operation.
4.3. Fuzzification
In the present work, the error and change in error of speed are fuzzified. Seven linguistic
fuzzy sets with triangular membership function are used, as shown in Figure 5. The seven
sets used for fuzzy variables ‘error’ and ‘change in error’ are negative big (NB), negative
medium (NM), negative small (NS), zero (Z), positive big (PB), positive medium (PM),
and positive small (PS).
4.4. Defuzzification
The reverse of fuzzification is called defuzzification. Weighted average method of de-
fuzzification suitable for Sugeno type controllers is used in this work. The defuzzified
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Chopper Drive Using Fuzzy Controller 913
Figure 5. Fuzzy memberships used.
output is the change in duty cycle. The new duty cycle is calculated by adding the existing
duty cycle and the change in duty cycle calculated after defuzzification.
4.5. Rule Table and Inference Engine
The fuzzy rules used in the design are in the general “If–Then” format. If error is Ai ,
and change in error is Bi , then output is Ci . Here the “if” part of a rule is called the
rule-antecedent and is a description of a process state in terms of a logical combination
of fuzzy linguistic sets. The “then” part of the rule is called the rule consequent and is
a description of the control output in terms of a logical combinations of fuzzy sets. The
designed fuzzy controller increases the change in duty cycle when the error is positive
and decreases the same when the error is negative.
4.6. Quadrant Selection Control
The four-quadrant operation is decided by the reference speed of the motor and the actual
speed at which the motor is running. If the reference speed is changed from positive to
negative or vice versa, then the quadrant control will select the corresponding quadrant.
Here, the positive value of speed is considered for forward direction and negative values
for reverse direction of the motor. The quadrant selection will decide the gating signals
for the switches.
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Table 2
110 V DC motor parameters
DC motor parameters Value
DC supply voltage 110 V
Armature resistance Ra 1 �
Armature inductance La 46 mH
Inertia constant J 0.093 Nm2
Damping constant B 0.008 Nm/rad/s
Torque constant parameter 0.55 Kt
Back emf constant 0.55 Km
Speed 1500 rpm
5. Simulation Results
The fuzzy toolbox is used to test and evaluate the fuzzy controller proposed. The simu-
lation was done for a chopper-fed DC motor with the fuzzy controller. The parameters
of the DC motors used are given in Tables 2 and 3. The computer simulation is done for
a step change in motor reference speed and the actual change in speed is recorded. The
step change in load torque is also applied and the corresponding change in the speed is
recorded.
The simulation is done based on equation modeling technique in MATLAB/simulink
toolbox. The complete model developed is given in Figure 6. The simulated graph of
normalized speed change from C1800 rpm to �1800 rpm in a 220 V motor with rated
load is given in Figure 7. The reference speed is initially 1800 rpm for 5 sec. So the
motor is operated in first quadrant (i.e., forward motoring) for first 5 sec and then the
reference speed is changed to �1800 rpm, and so the braking is applied and the motor
is operated in second quadrant (i.e., forward braking). After the motor speed becomes
zero, the motor is operated in the third quadrant (i.e., reverse motoring).
The simulated graph of normalized speed change from �1800 rpm to C1800 rpm
in a 220 V motor is given in Figure 8. The reference speed is initially �1800 rpm for
5 sec. So the motor is operated in third quadrant (i.e., reverse motoring) and then the
set speed is changed to C1800 rpm. So the braking is applied and the motor is operated
Table 3
220 V DC motor parameters
DC motor parameters Value
DC supply voltage 220 V
Armature resistance Ra 0.6 �
Armature inductance La 0.008 H
Inertia constant J 0.011 Nm2
Damping constant B 0.004 Nm/rad/s
Torque constant parameter 0.55 Kt
Back emf constant 0.55 Km
Speed 1800 rpm
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Figure 6. Simulink model of the proposed system.
Figure 7. Graph of normalized speed variation for the step change in reference speed from C1800
rpm to �1800 rpm with rated load.
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Figure 8. Graph of normalized speed variation for the step change in reference speed from �1800
rpm to C1800 rpm at rated load.
in the fourth quadrant (i.e., reverse braking). After the motor speed becomes zero, the
motor is operated in the first quadrant (i.e., forward motoring).
The simulated results in Figures 9 and 10 show the speed regulation for a step
change in the reference speed and the load applied. The reference speed is changed in
two steps from 0 to 60% of rated speed at 0 sec and then to 100% of rated speed at
3 sec. The load applied to the motor is changed from 0 to 100% at 6 sec. Figure 9 shows
the speed response for 110 V motor with the parameters in Table 2, and Figure 10 shows
the speed response for 220 V motor with the parameters in Table 3. These results show
the effectiveness and the advantage of the proposed fuzzy controller. The same fuzzy
controller works effectively on both 110 V and 220 V motors with the only change in
the reference current given to the inner current controller.
6. Embedded System Implementation
The fuzzy controller was implemented practically using Cygnal 8051 based processor
(C8051F005) and the required software was developed in C language. A four-quadrant
chopper was built with the MOSFET switches. A tacho generator was used to sense the
speed.
The micro controller (C8051F005) has 8051 compatible core with the following
features: 12-bit eight-channel ADC; two 12-bit DACs, two comparators, 2-kB data RAM,
and 32-kB flash memory. It also has an in-built PWM waveform generator available as
a programmable counter array. The PWM is generated at a frequency of 14 kHz. This
PWM waveform is then level amplified and fed to the DC-DC power converter through
IR2110 isolator chip. The chopper output is used to supply the armature of the DC motor
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Figure 9. Graph of normalized speed variation for the step change in the reference speed from 60
to 100% applied at 3 sec and step change in load torque from 0 to 100% applied at 6 sec for an
110 V motor.
Figure 10. Graph of normalized speed variation for the step change in the reference speed from
60 to 100% applied at 3 sec and step change in load torque from 0 to 100% applied at 6 sec for
a 220 V motor.
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Figure 11. Hardware setup used.
whose speed is to be controlled. The tacho-generator connected to the motor shaft gives
a DC voltage proportional to the speed and this DC voltage is fed to the ADC available
in the micro-controller. The photo of hardware setup used is given in Figure 11.
The flowchart of the control software used is given in Figure 12. For testing purposes,
the motor was run initially at half the rated speed and then after about 5 sec to rated
speed. The simulated graph of motor with fuzzy controller is given in Figure 13 and the
experimental graph of motor speed for the similar condition is given in Figure 14. The
experimental graph agrees with the simulated waveforms and confirms the use of FLC
for the control of motors.
7. Conclusion
This article presents the development and implementation of a real-time and low-cost
fuzzy controller for an embedded DC drive system. The fuzzy controller is implemented
in an 8051-based embedded micro-controller system. The advantage of this system is
that it does not require the mathematical model of the system for closed loop control. It
also has the advantage of reduced time for implementation after design. As embedded
system of programming is used, the system has the advantage that it can be easily
reconfigured at any time and reprogrammed according to the end use. Also, it is a low-
cost implementation of the closed loop fuzzy controller.
The fuzzy controller operation was tested for two different motors and was found to
have satisfactory response for both the motors. Thus, it is evident that the fuzzy controller
can be used for any motor with only modification in the reference current setting. The
dynamic response of DC motor speed variation with fuzzy controller was tested with
practical implementation and found to be giving satisfactory results.
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Figure 12. Flowchart of the control algorithm.
Figure 13. Simulated waveform of speed variation for the step change in reference speed.
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Figure 14. Experimental waveform obtained for step change in reference speed.
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