TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Performance Evaluation of Fuzzy Logic
Controlled Bidirectional DC to DC Converter
1
B. L. Narasimharaju, 2Satya Prakash Dubey, 3S. P. Singh
Indian Institute of Technology, Roorkee
1
narasimharaju.bl@gmail.com, 2simhadee@iitr.ernet.in, 3spd1020@gmail.com
Abstract—This paper presents the systematic design
procedure of fuzzy logic controller for the coupled inductor
bidirectional DC-DC converter. The converter contains nonlinear elements making the mathematical model more
complex which instigate the use of FLC. The design
guidelines, and FLC based voltage controller design of the
bidirectional converter is discussed in detail. The proposed
FLC for bidirectional DC-DC converter is validated through
simulation in Matlab/Simulink environment. The closed loop
results of the converter is presented and discussed.
Simulation results demonstrate that the converter can be
regulated with good performance irrespective of source and
load disturbance.
overcome the above problems, high performance coupled
inductor DC-DC converters are proposed [8-14] which
gives high-efficiency, high-voltage diversity without
utilizing extreme duty ratio. Therefore, proposed BDC
topology could be more attractive for small power grid in
renewable energy conversion systems. Power converters
provide a highly efficient means to deliver a regulated
voltage from a standard power source. In addition, these
converters/regulators are susceptible to various
disturbances from the attached load or the power source.
These disturbances, if not controlled, may damage or
shutdown devices attached to the converter. To maintain
the output voltage constant, irrespective of load and line
disturbances, it is necessary to operate the converter as a
closed loop system. In fact, the very familiar classical PI
controller is required to be tuned for acceptable dynamic
response, where as in the sample based controller only
gain is to be adjusted for a given system to regulate
overshoot. Also, in recent years, the Fuzzy Logic Control
(FLC) has become more popular in many applications.
These FLCs provides a method of nonlinear control using
piece-wise linear functions to apply varying gains
depending on the error signal between the desired output
and the actual output. FLCs have already been
incorporated in various DC-DC converter systems [16-18]
and applications [19-24]. The fuzzy logic controller is
advantageous over classical controls where the gains are
fixed. An FLC allows the proportional, integral, and
derivative gains to be adjusted to work optimally to
control the system and therefore make it a suitable for
control of DC-DC converters [16-18]. The fuzzy logic
[19-24] based controller gives nonlinear control with fast
response and virtually no overshoot. In this paper,
operation, and FLC based voltage controller design of the
proposed BDC topology is discussed and then validated
through simulation. The paper organized in four sections.
Starting with Section I, the others sections cover the
converter and controller designs in Section-II, simulation
results and analysis in Section-III, and conclusive
observations in Section-IV.
I. INTRODUCTION
THE DC-DC conversion technique has been greatly
developed to achieve high-efficiency, cheap topology in
simple structure and without extreme duty ratio. The
uninterruptible power supply (UPS) systems employed in
computer industry typically provide ten to thirty minutes
reverse time, which is highly inadequate for
telecommunication system. The convergence of computer
and telecommunication industries makes the 48V DC
battery plant as an innate choice to offer long backup
during AC mains‟ outages [1-3]. It is more economical for
storage batteries to use a few large-capacity cells than
many small-capacity cells, and for that reason the number
of cells used in small/micro power grid is limited.
Usually, inverters used in DC-UPS (DUPS) systems
require comparatively high input DC voltages of about
240V; which necessitates voltage step up when
discharging batteries, and step down when charging them.
Thus, batteries need high voltage diversity ratio in
discharging, and charging modes. The above
shortcomings can be fulfilled by using bidirectional DCDC (BDC) converters between the storage devices and
grid supply/load. However, conventional based BDC
topologies [4-6] are well reported in the literature.
Unfortunately, switches of four and beyond in isolated
and cascading non-isolated topologies increase production
costs. Also, high rated devices are required to develop
relatively low voltages which lead to high voltage/current
stress on the devices, and reduced efficiency. In addition,
the major concerns related to the efficiency of the DC
backup converter; large input current, high output voltage
and rectifier reverse recovery problem. Thus, circuit
trends are requiring voltage/current requirements outside
the efficient range of most classical converters; the duty
cycle is below 0.1 or above 0.9, and therefore new
converter topologies must be developed [7]. In order to
II. CONVERTER AND CONTROLLER DESIGNS
A. Converter Operation and Design
The BDC converter operation can be identified in two
modes. One is discharge mode during which the BDC is
used to boost the battery voltage to a suitable high level
DC bus voltage. Second is the charging mode during
which the BDC is used to buck the DC bus voltage to a
598
TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Mode-3 and mode-4 are similar to the mode-2 and
mode-1 respectively. Hence, the converter operation is
now considered to be in reverse buck mode during which
switch (S2) is pulse modulated and the diode D1 is the
freewheeling device. The secondary current (IL2) is from
the DC bus by way of the two series windings (L1and L2)
of the coupled inductor to charge the battery in the LV
side. As the converter operation is chosen for continuous
current mode (CCM), the relationship between the VLv
and the VHv can be attained with volt-second balance
principle. It can be expressed by
(V
 VLV )(1   1 )T
(1)
VLV  1T  HV
0
( N  1)
From (1), the DC gains of the boost mode (G1) and
buck mode (G2) is developed as
(1  N1 )
(1  1 )
(2)
G1 
G2 
(1  1 )
(1  N1 )
Where δ is the duty ratio of the switch S1 for boost
mode and δ2=(1-δ1) is the duty ratio of the switch S2for
buck mode, T is the switching period, and N  n2 is the
suitable low level battery voltage. The converter operation
in continuous conduction mode (CCM) is a suitable
choice to get a better dynamic response and also a tight
regulation of output voltage for the entire load variation.
The proposed bidirectional DC-DC converter topology is
depicted as in fig.1 (a). The converter operation is
categorized into four modes. In mode-1 and mode-2
converter operates in forward boost mode, the power flow
is from battery to DC bus. In mode-3 and mode-4 the
converter operates in reverse buck mode, the power flow
will reverses and is now from the DC bus to battery. The
characteristic waveforms of both boost and buck modes
are depicted in Fig. 2. The major symbol representations
are summarized as follows. VLV and VHV, respectively,
denote the voltages at the low-voltage (LV) and highvoltage (HV), L1and L2 represent individual inductors in
the primary and secondary sides of the coupled inductor
respectively, where the primary side is connect to a
battery module. The symbols S1 and S2 are the lowvoltage step-up switch and high-voltage step-down switch
respectively. The operation in mode-1 and mode-2 is
equally valid for mode-4 and mode-3 respectively. Thus,
only operation in mode-1 and mode-2 is described as
follows:
1. Mode-1[fig. 1(b)]
n1
turn‟s ratio of the coupled inductors L1 and L2.
The coupled inductor in Fig. 1 can be modelled as an
ideal transformer including the magnetizing inductors
(Lm1 and Lm2) and leakage inductors (Lk1 and Lk2) those
are not shown in the fig. The coupling coefficients (k1 and
k2) of ideal transformer are defined as
Lm1
L
Lm 2
L
k1 
 m1 k2 
 m 2 (3)
( Lm1  Lk1 ) L1
( Lm 2  Lk 2 ) L2
The coupling coefficients is simply set at one to
obtain Lm1=L1 and Lm2=L2 via (3). The series windings
(L1and L2) and their mutual inductance (M) can be taken
as a single inductor, and the equivalent magnetizing
inductor (Lm) can be represented as
1
L  (1  N ) 2 L  (1  ) 2 L  ( L  L  2M ) (4)
In this mode, the LV side switch (S1) is conducting
for TON time. Because the inductor is charged by the
battery, the magnetizing current increases gradually in an
approximately linear way. The secondary current is zero
since diode D2 is reverse biased.
2. Mode-2[Fig. 1c)]
In this mode, the LV side switch (S1) is turned off for
TOFF time. Thus reverses the polarities of the coupled
inductors. The diode D2 gets forward biased and the mode
begins when the primary current equals the secondary
current. The battery and the coupled inductor are
connecting in series to discharge into the HV DC bus
through the diode D2 by way of a low current type.
Because the inductor is supplied to the DC bus, the
magnetizing current decreases gradually in an
approximately linear way.
L1(n1)
iL1(t)
ILv
+
VLv
_
CL
rL
icL(t)
+
VcL(t)
_
+
L2 (n2)
_
VL1
+
VL2
iL2(t)
_
+
ich(t)
_
S1
L1 pk
IHv
VHv
+
Vch(t)
R
CH
L1(n1)
iL1(t)
+
VLv
_
CL
+
rL
VL1
_
0V
L2 (n2)
+
VL2
+
ich(t)
-NVLv
icL(t)
+
VcL(t)
_
_
IHv
VHv
+
Vch(t)
CH
R
_
(b) When S 1ON & D2OFF (Discharge mode) or D 1ON & S 2OFF (Charge mode)
L1(n1)
iL1(t)
ILv
+
VLv
_
CL
rL
icL(t)
+
VcL(t)
_
+
VL1
_
L2 (n2)
+
VL2
(VHv+NVLv)/(1+N)
iL2(t)
_
+
ich(t)
+
Vch(t)
_
IHv
VHv
CH
2
1
2
L1 min
L 2 pk
(1  N ) L1 min
VLV (1  1 )1T
(6)
 I HV
2( N  1) L1 min
Since the output current (IHV) in boost mode is equal
to the inductor (L2) average current, therefore, minimum
value of inductance (L1min) can be obtained as
V (1  1 )1T
(7)
L1 min  LV
2 I HV VHV ( N  1)
For continuous current conduction the selected value
of L1  L1 min and vice versa for discontinuous current
conduction. Finally equivalent inductance (Lm) and
secondary coupled inductor (L2) can be calculated from
I L 2 av 
iL2(t)
_
N
From (5), the average current through L2 is developed
as
_
(a) Proposed Coupled Inductor Bidirectional DC-DC Converter
ILv
1
Mutual inductance M  K L1L2 =NL1 for 100%
coupling (i.e. K=1) the equivalent inductor (L) is larger
than the value of (L1 or L2) to limit the ripple and
ascendant rates of the charge current. Assuming boundary
between CCM and discontinuous current mode (DCM)
(i.e. iL1 min  0 ); the peak values of the inductor currents
IL1Pk and IL2Pk can be expressed as
V T
VLV 1T
(5)
I
 LV 1
I

S2
D2
D1
m
R
_
(c) When D2ON & S 1OFF (Discharge mode) or S 2ON & D1OFF(Charge mode)
Fig. 1,2,3: Proposed BDC Converter and Equivalent Circuits
for Operating Modes
599
TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
The input variables are converted into labels of fuzzy
sets in terms of suitable linguistic values, this is called
fuzzification process. The scaled inputs are crisp values
limited to the universe of discourse of input variables. The
Membership Function (MF) is used to convert each of
input variables into membership value between 0 and 1.
The overall performance of the system is affected by the
shapes and number of the MFs that are chosen according
to the experience of expert people about the process. The
MFs may take any arbitrary shape or form, such as
triangular functions, sigmoidal curves, Gaussian
distribution curves, trapezoidal functions and exponential
shapes.
The several procedures are reported [18-24] that can
be used to build MFs. The triangular MFs are chosen to
evaluate the degree of membership of the input crisp
values. The output of the fuzzy controller is the control
signal „u‟ which is used to produce modulating pulses
which drives the switches of the BDC converter. The
proposed fuzzy system consists of seven MFs for error
(E), change in error (CE), and seven MFs for output
control signal (u). A number of fuzzy reasoning methods
are reported in the literature [19-21] such as; mamdani‟s,
larsen‟s, sugeno‟s and tsukamoto‟s methods. Herein this
work, mamdani fuzzy reasoning method is used to obtain
the inference result from a system. The fuzzy reasoning
strategy of this method is based-on the MAX-MIN
composition. Fig.3 illustrates the fuzzy input MFs plots of
the variables E (voltage error), and CE (change in error)
respectively. Fig.5 illustrates fuzzy output control
variable. The each input and output MFs are divided into
seven linguistic variables namely NB(negative big), NM
(negative medium), NS (negative small), Z (zero), PS
(positive small), PM (positive medium) and PB (positive
big). The output MFs are asymmetrical because near the
origin the signal requires more precision. The knowledge
base is defined in the form of linguistic rules. By naming
the numbered symbols (0→zero, 1→positive small, 2→
positive medium..., -1→ negative small, -2→ negative
medium...), we recognize the classical antidiagonal rule
base [24] proposed by Macvicar-Whelan. The Table-I
shows the corresponding rule table for the controller. The
top row and left column of the matrix indicate the fuzzy
sets of the variable E and CE respectively, and the MFs of
the output variable (dU) are shown in the elements of the
matrix.
(4). In Fig. 1 the filter capacitors CL and CH are used on
the LV battery side and HV DC-bus side respectively to
achieve ripple free voltages.
B. Fuzzy Logic Controller Design
The dynamics of DC-DC converters is non-linear
with uncertain parameters owing to uncertain output
voltage during the operation. The practical converter
operation deviates from theoretical prediction because of
problems associated with parasitic resistances, stray
capacitances and leakage inductances of the components.
In order to obtain the desired operating voltage
irrespective of the source and the load disturbances, the
converter must be operated in closed loop. All these
problems are efficiently dealt with in FLC. Fuzzy control
method does not need accurate mathematical model of a
plant, and therefore, it suits well to a process where the
model is unknown or ill-defined [20, 21]. Even when the
plant model is known, there may be parameter variation
problem. In general, fuzzy expert system is applicable
wherever the knowledge base of expert system contains
fuzziness.
The converter control regulates nominal operating
point of the converter. Fig.3 depicts the block diagram of
the fuzzy control scheme for BDC converter. The output
voltage amplitude is determined and compared with
reference voltage, which is taken as proportional to the
rated terminal voltage of the BDC converter. The voltage
error (E) and change in voltage error (CE) is determined
and processed through FLC.
The resulting output is processed through a PWM
generator where it is compared with symmetrical
triangular wave to obtain suitable pulse. The fuzzy
controller [17,18] is divided into five sections: fuzzifier,
knowledge base, rule base, decision making and
defuzzifier as shown in fig.3. The fuzzifier converts crisp
data into linguistic format. The inference system decides
in linguistic format with the help of logical linguistic rules
supplied by the rule base and the relevant data supplied by
the data base. The output of the inference system passes
through the defuzzifier wherein the linguistic format
signal is converted back into the numeric form or crisp
form. The inference system block uses the rules in the
format of “if-then-else”. The inputs of the fuzzy controller
are the error and change in error is defined as follows;
(8)
E (n)  Vref (n)  Vo (n)
(9)
CE (n)  E (n)  E (n  1))
Where, Vref and Vo are terminal voltage and
reference voltages at nTh sampling time.
Degree of membership
NB
1
Input (Vi)
BDC Converter
VO

Rule Base
PWM Generator
with Comparator
VRef

E(n)

E(n)

CE(n)

Fuzzifier
Decision
Making
Defuzzification
u(n)=u(n-1)+du(n)
E(n-1)
Z-1
NS
Z
PS
PB
-0.5
0
E / CE
0.5
1
0.8
0.6
0.4
0.2
0
Knowledge
Base
Fuzzy Logic Controller
-1
Fig. 4: Membership Functions of Error Input (E) and Change in Error
Input (CE)
Fig. 3: Fuzzy Logic Control Scheme for BDC Converter System
600
TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Degree of membership
NB
1
NM
NS
ZE
PS
PM
III. SIMULATION RESULTS AND ANALYSIS
PB
For justification of the proposed BDC converter
operation, and closed loop FLC performance;
specifications, and design values are obtained as described
in table-II. The Simulink model of the BDC converter
with voltage mode control has been developed as depicted
in fig.6. Thus, the performance analysis in open loop and
closed loop has been evaluated extensively.
0.8
0.6
0.4
0.2
0
-1
-0.5
0
dU
0.5
1
TABLE-II
DATA FOR THE PROPOSED COUPLED BDC CONVERTER
Parameter
Boost Mode
Buck Mode
Input voltage
VLV=24V
VHV=200V
Output voltage
VHV= 200V
VLV=24V
∆Vo (Ripple)
≤0.5%
≤0.5%
Output Power
400 Watt
384 Watt
Output Current
2A
16A
Switching Frequency
50 kHz
Turns Ratio
2
Duty Cycles
δ1=0.71, δ2=(1- δ1)
Inductors
L1=50µH, L2=200µH, M=98µH
Resistors
RHV=100 Ω, RLV=1.5 Ω, rL=0.001Ω,
Capacitors
CL=CH=10µF
Fig. 5: Membership Functions of Control Output (u)
To maintain the voltage at desired level the triangular
membership of error and control output are cramped near
to zero for the given operating condition. For improving
the controller performance, membership functions are
further adjusted based on trial and error procedure. The
sensitivity of a variable determines the number of fuzzy
subsets, respectively. Before fuzzification, the input
variables are normalized with respect to reference voltage
(Vref). This gives the system an adaptive characteristic and
enables the optimal operating point to be found
effectively.
The per unit (PU) values of the MF boundaries have
been chosen by considering the characteristics of the
converter. For example, the boundaries of the small fuzzy
set are 0 and 0.02, which covers all the errors and
derivative errors with value less than 2 percent of the
reference value. The fuzzy inference includes the process
of fuzzy logic operation, fuzzy rule implication and
aggregation. In the fuzzy inference system, the fuzzified
input variables are processed with fuzzy operators, and the
IF-THEN rule implementation. The proposed system has
25 (7x7) possible rules as described in Table-I that can
build by crossing the fuzzy sets considered for each input.
Where a rule read as:
(10)
if E  NB and CE  Z then du  NM
E(pu)
CE(pu)
NB
NS
Z
PS
PB
TABLE I: FUZZY RULE BASE MATRIX
NB
NS
Z
PS
NB
NB
NM
NS
Z
NB
NM
NS
Z
PS
NM
NS
Z
PS
PM
NS
Z
PS
PM
PB
PB
Z
PS
PM
PB
PB
Fig. 6: Simulink Schematic of the FLC Based BDC Converter
Fig.7 illustrates the open loop steady-state
performances of proposed BDC converter. These critical
analyses and observations confirm improvements of the
proposed topology in providing high voltage diversity.
Also, justifies the improved utilization factor of switches.
Fig.8 to fig.10 illustrates the closed loop performances of
the converter for various load conditions. The voltage
controller increases the immunity of the converter output
voltage to changes in the input voltage and load current.
The load regulations under wide range of load conditions
have been made extensively. As shown in fig. 9 and fig.
10, the effect of step load change are observed at
0.0075sec for boost mode, and at 0.0225 sec for buck
mode respectively. The closed loop result analysis clearly
shows that at any instants of load changes, output voltage
get stabilizes at faster rate to the desired value (200V) in
boost with small overshoot. Similarly, in buck mode
output voltage get stabilizes at very faster rate to the
desired to value (24V) in buck mode with smaller
overshoot particularly at light load conditions. Thus, it
The value of output signal is determined in
accordance with the linguistic rules. The required rules
and data are supplied by the rule base. The linguistic
output data is converted back into crisp output data by
defuzzification. The membership of the corresponding
output is taken as minimum membership value for the two
respective inputs. Mathematically,
(11)
  min  (input1)  (input 2)
 p(m)
(12)
Crisp Output 

Where, µ refers to membership value, the output
membership is stored in α and p(m) refer to location of
peak of membership function. The defuzzified (crisp)
value multiplied by a scale factor and integrated to obtain
the output (u).
601
TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
Fig. 9: Closed loop Performances for Full Load and Over Load
20
10
---->I(L1), I(L2)
0
0.012
30
0.0121
0.0122
I(L1)
I(L2)
20
10
0.0122
200
---->VHv
5
0
0.022 0.0221 0.0221 0.0221 0.0222
0
---->VHv
0.0121
0.0122
Time (t)
-10
I(L1)
I(L2)
-30
0.022 0.0221 0.0221 0.0221 0.0222
---->Vg1, Vg2
0
---->i(L1), i(L2)
50
0.025
0.03
Buck Mode
Boost Mode
-50
---->i(S1), i(S2
---->V(S1)
---->V(S2)
0.02
0
0
0.005
0.01
0.015
Switch Current's
0.02
0.025
0.03
0
0.005
0.01
0.015
Switch Voltage
0.02
0.025
0.03
0
0.005
0.01
0.015
Switch Voltage
0.02
0.025
0.03
0
0.005
0.01
0.015
------> Time (t)
0.02
0.025
0.03
40
---->VHv
0.01
0.015
0.02
0.025
0.03
0
0.005
0.01
0.015
0.02
0.025
0.03
0
0.005
0.01
0.015
------> Time (t)
0.02
0.025
0.03
10
0
200
0
200
100
0
Boost Mode
Buck Mode
0
0.005
0.01
0.015
0.02
0.025
0.03
0
0.005
0.01
0.015
0.02
0.025
0.03
0
0.005
0.01
0.015
0.02
0.025
0.03
0.005
0.01
0.015
------> Time (t)
0.02
0.025
0.03
4
2
0
40
20
0
20
10
0
0
J. Akerlund “48V DC computer equipment topology – An
emerging technology,” Proc. of IEEE-INTELEC, pp. 15–21, 1998.
[2] T.M. Fruzs and J. Hall “AC-DC or hybrid power solutions for
today‟s telecommunications facilities,” Proc. of IEEE-INTELEC,
pp. 361–368. 2000.
[3] L. Huber and M.M. Jovanovic, “A design approach for server
power supplies for networking,” Proc. of IEEE-APEC, pp. 1163–
1169, 2000.
[4] S. Lee, and G.T. Cheng, “Quasi-Resonant Zero-Current-Switching
Bidirectional Converter for Battery equalization Applications,”
IEEE Trans. on Power Electronics, Vol.21, No.5, Sept 2006, pp.
1213–1224.
[5] S. Waffler and I.W. Kolar, “A Novel Low-Loss Modulation
Strategy for High-Power Bi-directional Buck Boost Converters”,
Record of 7Th International Conf. on Power Electronics, pp. 889–
894, Oct 2007.
[6] L.R. Chen, N.Y. Chu, C.S. Wang, and R.H. Liang, “Design of a
Reflex-Based Bidirectional Converter with the Energy Recovery
Function,” IEEE Trans. on Industrial Electronics, Vol.55, No.8,
August 2008, pp. 3022–3029.
[7] B. Axelrod, Y. Berkovichm and A. Ioinovici, “SwitchedCapacitor/Switched-Inductor Structures for Getting Transformer
less Hybrid DC-DC PWM Converters," IEEE Trans. on Circuits
and Systems, Vol. 55, No.2, March 2008, pp. 687–696.
[8] Q. Zhao and F.C. Lee, “High performance coupled-inductor DCDC converter,” Proc. of IEEE APEC Conf., pp. 109–113, 2003.
[9] Q. Zhao and F.C. Lee, “High-efficiency, high step-up DC-DC
converters,” IEEE Trans. Power Electronics, Vol.18, 2003, pp. 65–
73.
[10] R.J. Wai and R.Y. Duan, “High-efficiency bidirectional converter
for power sources with great voltage diversity,” IEEE Trans. Power
Electron., Vol. 22, No.5, September 2007, pp. 1986–1996.
[11] R.J. Wai and R.Y. Duan, “High Step-Up Converter with CoupledInductor”, IEEE Trans. on Power Electronics, Vol.20, No.5, Sept
[1]
150
100
50
Closed Loop FLC Performances
---->IHv
0.005
REFERENCES
0
300
---->VLv
0
20
Fig. 8: Closed Loop Performances with Load Changes
---->ILv
0.03
The design analysis of the proposed converter has
been discussed. The proposed converter circuit offers
wide range of voltage diversity, and effective switch
utilization factor. Also, it requires only two switches to
achieve the bidirectional power flow as compared to the
isolated bidirectional bridge converters. The model of the
BDC converter with FLC based voltage mode control has
been developed in Simulink environment. Thus, the
steady-state, and transient performance analysis in open
loop and closed loop has been evaluated extensively. The
closed loop regulation is evaluated for wide range of load
conditions which show the excellent performances of
fuzzy logic controller.
0.5
0.015
Inductor Current's
0.025
IV. CONCLUSION
0
0.022 0.0221 0.0221 0.0221 0.0222
Time (t)
Gate Pulse Voltage's
0.01
0.02
Fig. 10: Closed loop Performances for Full Load and Light Load
1
0.005
0.015
40
20
0
-20
-40
0
Fig. 7: Current and Voltage Waveforms of Proposed BDC Converter
0
0.01
20
10
0
0.012
Buck Mode
0.005
2
0
20
100
Boost Mode
0
4
---->VLv
0.0121
100
0
-20
0
0.012
200
---->IHv
0.0122
---->I(S2)
0.0121
Buck Mode Coupled BDC
300
VD1
250
200
VDS2
150
100
50
0
0.022 0.0221 0.0221 0.0221 0.0222
10
---->VLv
VDS1
VD2
300
---->ILv
Boost Mode Coupled BDC
---->I(L1), I(L2)
---->I(S1)
300
250
200
150
100
50
0
0.012
30
Closed Loop FLC Performances
---->VD1, VDS2
---->VDS1, VD2
justifies the excellent load regulation in both modes of
operation and also the dynamic performances.
602
TECHNIA – International Journal of Computing Science and Communication Technologies, VOL. 3, NO. 2, Jan. 2011. (ISSN 0974-3375)
2005, pp. 1025–1035.
[12] K. Yao, M. Ye, M. Xu, and F. C. Lee, “Tapped-Inductor Buck
Converter for High-Step-Down DC-DC Conversion IEEE Trans.
on Power Electronics, Vol.20, No.4, July 2005, pp. 775–780.
[13] J. Calvente, and L.M. Salamero, et al., “Using magnetic Coupling
to eliminate Right Half-plane Zeros in Boost Converters,” IEEE
Trans. On power Electronics letters, Vol.2, No.2, June 2004, pp.
58–62.
[14] S. Dwari and S. Jayawant, et al., “Dynamic Characterization of
Coupled-Inductor Boost DC-DC Converters”, IEEE Proceedings
on COMPEL06, pp. 264–269, 2006.
[15] P. Thounthong, S. Rael, and B. Davit, “Control algorithm of fuel
cell and batteries for distributed generation system,” IEEE Trans.
Energy Conv., Vol. 23, No.1, March 2008, pp. 148–155.
[16] W.C. SO and C.K. TSE, “Development of a fuzzy logic controller
for DC/DC converters: Design, computer simulation, and
experimental evaluation,” IEEE Transactions on Power
Electronics, Vol.11, 1996, pp.24-31.
[17] T. Gupta, R.R. Boudreaux, R.M. Nelms and J.Y. Hung
„Implementation of a fuzzy controller for DC-DC converters using
an inexpensive 8-b microcontroller‟, IEEE Trans on Industrial
Electronics, Vol.44, No.5, 1997, pp.661-669.
[18] P. Mattavelli and G. Spiazzi, “General purpose fuzzy controller for
DC-DC converters”, IEEE Trans. on Power Electronics, Vol.12,
No.1, 1997, pp.79-86.
[19] T. Takagi and M. Sugeno, “Fuzzy identification of a system and its
applications to modeling and control”, IEEE Trans. on System,
Man, and Cybernetics, Vol.15, 1985, pp.116-132.
[20] B.K. Bose, “Modern power electronics and AC drives”, Pearson
Education Inc., 2002.
[21] B.K. Bose, “Expert systems, fuzzy logic and neural network
applications in power electronics and motion control”, IEEE Proc.,
Vol.82, 1994, pp.1302-1323.
[22] C.C. Lee, “Fuzzy logic in control system: Fuzzy logic controllerpart I”, IEEE Trans. on System, Man, and Cybernetics, Vol.20,
No.2, 1990, pp.404-418.
[23] C.C. Lee, “Fuzzy logic in control system: Fuzzy logic controllerpart II”, IEEE Trans. on System, Man, and Cybernetics, Vol.20,
No.2, 1990, pp.419-435.
[24] P.J. Macvicar-Whelan, “Fuzzy sets for man–machine interaction,”
International Journal of Man-Machine Studies, Vol.8, 1976, pp.
687–697.
603