This paper proposes a combined fuzzy adaptive slidingmode voltage controller (FASVC) for a threephase UPS inverter. The proposed FASVC encapsulates two control terms: a fuzzy adaptive compensation control term, which solves the problem of parameter uncertainties, and a slidingmode feedback control term, which stabilizes the error dynamics of the system. To extract precise load current information, the proposed method uses a conventional load current observer instead of current sensors. In addition, the stability of the proposed control scheme is fully guaranteed by using the Lyapunov stability theory. It is shown that the proposed FASVC can attain excellent voltage regulation features such as a fast dynamic response, low total harmonic distortion (THD), and a small steadystate error under sudden load disturbances, nonlinear loads, and unbalanced loads in the existence of the parameter uncertainties. Finally, experimental results are obtained from a prototype 1 kVA threephase UPS inverter system via a TMS320F28335 DSP. A comparison of these results with those obtained from a conventional slidingmode controller (SMC) confirms the superior transient and steadystate performances of the proposed control technique.
I. INTRODUCTION
Nowadays, the nonlinear nature of electric loads is resulting in a strong demand for highquality and reliable power sources from both customers and utilities
[1]

[5]
. To address this issue, uninterruptible power supplies (UPSs) are being extensively employed for critical loads such as communication systems, medical support systems, emergency systems, etc.
[6]
. For improving power quality through UPS systems, it is important to achieve sinusoidal output voltage waveforms with a fast voltage recovery capability and a very low total harmonic distortion (THD) regardless of the type of load. Typically, an inverter with an
LC
output filter in a UPS system is a suitable solution to fulfill this requirement. The main criteria for evaluating the regulation performance of the UPS inverter output voltage are a quick dynamic response, low THD, and small steadystate error. Moreover, various load conditions such as abrupt load changes, nonlinear loads, and unbalanced loads seriously harm the performance of UPS inverters. Thus, an appropriate control strategy is desired to sufficiently meet the performance criteria of UPS systems under any type of electrical load.
Recently, many researchers have presented a number of advanced control techniques for UPS systems
[7]

[20]
. The authors of
[7]
described a feedback linearization control scheme for the UPS inverter. This method focuses on achieving a low THD and a fast dynamic response without considering parameter uncertainties. In
[8]
and
[9]
, a repetitive control is applied to generate a highquality sinusoidal output voltage. In general, this technique has problems such as a slow transient response and instability to error dynamics. In
[10]
, a model predictive control is suggested for regulating the UPS output voltage. This method utilizes a load current observer and has a small steadystate error. However, it reflects a high THD value in the output voltage under linear and nonlinear loads. In
[11]
, a hybrid PID control scheme with multiple loops is proposed. With the advantages of ensuring a good performance and easy implementation, these techniques require many trials to tune for the proper gains. According to
[12]
, a deadbeat control technique can provide a fast dynamic response and high accuracy. However, this scheme is very sensitive to the parameter uncertainties. In
[13]
, an
H
_{∞}
loop–shaping control scheme is presented. This method has a simple structure and is robust under model uncertainties. Nevertheless, it is applied to a singlephase inverter. In
[14]
, an adaptive fuzzy control method is illustrated for a threephase UPS system. Although this approach tracks the desired sinusoidal waveform regardless of being subjected to nonlinear loads, the large number of fuzzy rules employed raises the computational burden. In
[15]
, a hybrid fuzzyrepetitive control is applied for the UPS inverter. This scheme reveals a good transient response under load disturbance; whereas it is applied to a singlephase UPS inverter and its THD value is high for nonlinear loads. Next, a slidingmode control method is employed on UPS inverters
[16]

[20]
. It is obvious from
[16]

[18]
that good UPS performance can be obtained by this control scheme. However, in
[16]
,
[17]
, the control scheme is only implemented on a singlephase inverter and in
[18]
the results for nonlinear loads are not provided. In
[19]
and
[20]
, the authors achieve a good voltage response, but the stability analysis is not presented.
This paper presents a fuzzy adaptive slidingmode voltage controller (FASVC) for a threephase UPS inverter. In this paper, the solution to parameter uncertainties is provided by a fuzzy adaptive compensation control term. In addition, the error dynamics of the system are stabilized by a slidingmode feedback control term. A conventional load current observer is utilized to precisely estimate the loadcurrent without using current sensors. The stability of the proposed FASVC is completely validated by the Lyapunov theory. Hence, in real applications, the proposed control technique can accomplish exceptional voltage regulation performance (such as a fast dynamic response, low THD, and small steadystate error) not only in the presence of parameter uncertainties, but also under sudden load changes, nonlinear loads, and unbalanced loads. A conventional slidingmode controller (SMC) is also tested to highlight the outstanding performance of the suggested control approach. Finally, the validity of the proposed FASVC is demonstrated by comparative experimental results carried out on a prototype 1 kVA threephase UPS inverter system using a TMS320F28335 DSP.
II. MATHEMATICAL MODELING OF THREEPHASE UPS INVERTER SYSTEM
A circuit diagram of the threephase UPS inverter system with an
LC
output filter is shown in
Fig. 1
. It consists of the following components: a DClink (
V_{DC}
), a threephase IGBT inverter, three filter capacitors (
C_{f}
), three filter inductors (
L_{f}
), and a threephase load.
The state equations of
Fig. 1
can be represented in the synchronously rotating
dq
reference frame by the following system model equations
[7]
:
Circuit diagram of a threephase UPS inverter system.
where
i_{id}
,
i_{iq}
,
v_{id}
, and
v_{iq}
are the
dq
axis inverter currents and voltages,
i_{Ld}
,
i_{Lq}
,
v_{Ld}
, and
v_{Lq}
are the
dq
axis load currents and voltages, and
are the time derivatives of the
dq
axis inverter currents and load voltages, respectively. In addition,
ω
denotes the system angular frequency (
ω
=
2πf
), and
f
is the fundamental frequency of the load voltage.
It should be noted that:

1)iidqandvLdqare the state variables and measurable;

2)vidqis the control input;

3)iLdqis the unknown disturbance and changes very slowly during a sampling period[10];

4) The desireddqaxis load voltagesvLd*andvLq*are constant during a sampling period and then their time derivatives can be set to zero.
III. FUZZY ADAPTIVE SLIDINGMODE VOLTAGE CONTROLLER DESIGN AND STABILITY ANALYSIS
This section thoroughly presents the proposed FASVC algorithm and its stability analysis by utilizing the system model (1). First, the errors of the
dq
axis inverter currents (
i_{id}
,
i_{iq}
) and load voltages (
v_{Ld}
,
v_{Lq}
) are defined as follows:
where
e_{id}
,
e_{iq}
,
i_{id}
^{*}
, and
i_{iq}
^{*}
are the errors and reference values of the
dq
axis inverter currents, respectively. Similarly,
e_{Ld}
,
e_{Lq}
,
v_{Ld}
^{*}
, and
v_{Lq}
^{*}
are the errors and reference values of the
dq
axis load voltages, respectively. Then, the following error dynamics can be derived:
where
u_{d}
,
u_{q}
,
v_{ind}
, and
v_{inq}
are the system uncertainty terms and the control inputs, respectively, and
γ_{d}
and
γ_{q}
denote positive numbers.
The system uncertainty terms (
u_{d}
,
u_{q}
) are given by:
It should be noticed from (4) that
u_{d}
and
u_{q}
contain the time derivative terms (
) that cannot be computed in a straightforward manner due to system noises. In addition, it is assumed that
L_{f}
has some uncertainties because of the nonlinear magnetic properties. In practice, the exact estimation of the uncertainty terms (
u_{d}
,
u_{q}
) is required instead of directly using the time derivatives of
i_{id}
^{*}
and
i_{iq}
^{*}
.
Next, the control inputs (
v_{ind}
,
v_{inq}
) can be written as:
where
u_{fad}
,
u_{faq}
,
u_{smd}
, and
u_{smq}
are the
dq
axis fuzzy adaptive compensation control terms and the
dq
axis slidingmode feedback control terms, respectively.
Proposition 1:
Assume that the filter capacitance
C_{f}
is known. In addition, let the compensation control terms (
u_{fad}
,
u_{faq}
) and the feedback control terms (
u_{smd}
,
u_{smq}
) be obtained by the following control laws as:
where
σ_{d}
and
σ_{q}
are sliding surfaces, and
τ_{d}
,
τ_{q}
,
ε_{d}
, and
ε_{q}
are positive constants. Then
e_{Ld}
and
e_{Lq}
converge to zero.
Proof:
The given control law (7) is known as the slidingmode control term. Its stability analysis can be divided into two tasks. The first task is to determine the stability of the reducedorder slidingmode dynamics, while the second task is to verify the reachability condition. By setting
, the secondorder slidingmode dynamics restricted to the sliding surface can be derived as:
which is asymptotically stable. Therefore, it should be shown that the reachability condition is satisfied. To this end, the Lyapunov function
V_{0}(t)
can be defined as:
Its time derivative is expressed as:
The following equation can be obtained from (3), (6), (7), and (8):
Substituting (11) into (10) yields:
The above compensation control law (6) requires accurate knowledge of
u_{d}
and
u_{q}
due to the parameter uncertainties. Therefore, the following
i^{th}
fuzzy rules for the two fuzzy models (
η_{d}
and
η_{q}
) are applied to approximate
u_{d}
and
u_{q}
, respectively.

Fuzzy rule iforηd: IFxjisFji, THENηdisSdi.

Fuzzy rule iforηq: IFxjisFji, THENηqisSqi.
where
j
=
1
,
2
,
3
,
4
, and
i
=
1
,
2
,…,
r
, and
r
is the total number of fuzzy rules.
x_{j}
represents the state variables (i.e.,
x_{1}
=
v_{Ld}
,
x_{2}
=
v_{Lq}
,
x_{3}
=
i_{id}
,
x_{4}
=
i_{iq}
).
F_{ji}
denotes the fuzzy sets associated with
x_{j}
.
S_{di}
and
S_{qi}
are the fuzzy singletons for
η_{d}
and
η_{q}
, respectively. The membership functions
g_{ji}
(
x_{j}
) are used to further characterize the fuzzy sets
F_{ji}
. The final output (
η_{d}
,
η_{q}
) of the above fuzzy models can be inferred by using a standard fuzzy inference method that consists of a singleton fuzzifier, a product fuzzy inference, and a weighted average defuzzifier.
where
ξ_{d}
= [
ξ_{d}
_{1}
, ··· ,
ξ_{dr}
]
^{T}
= [
S_{d}
_{1}
, ··· ,
S_{dr}
]
^{T}
and
ξ_{q}
= [
ξ_{q}
_{1}
, ··· ,
ξ_{qr}
]
^{T}
= [
S_{q}
_{1}
, ··· ,
S_{qr}
]
^{T}
are the adaptable parameter vectors, and
h
= [
h
_{1}
, ··· ,
h_{r}
]
^{T}
is the fuzzy basis function. Moreover, the vector
h_{i}
is considered as the normalized weight of each IFTHEN rule, which satisfies both
h_{i}
≥ 0 and
. Thus,
h_{i}
can be expressed as:
It is assumed that
ξ_{*d}
and
ξ_{*q}
are the optimal parameter vectors. According to standard results
[23]
,
[24]
, a fuzzy system can uniformly approximate nonlinear functions to an arbitrary accuracy. As a result, if the searching spaces for
η_{d}
and
η_{q}
are sufficiently large, the following inequalities can be assumed:
Now, the fuzzy adaptive compensation control terms (
u_{fad}
,
u_{faq}
) and the slidingmode feedback control terms (
u_{smd}
,
u_{smq}
) can be written by:
where
λ_{di}
and
λ_{qi}
are positive design parameters. Then, the following can be established:
Theorem 1:
Assume that the filter capacitance
C_{f}
is known. Let the control law be given by (16) and (17). Then
e_{id}
,
e_{iq}
,
e_{Ld}
, and
e_{Lq}
converge to zero, and
ξ_{di}
and
ξ_{qi}
are bounded.
Proof:
Since
Proposition 1
indicates that the linear sliding surface (8) guarantees the asymptotic stability of the slidingmode dynamics, it should be demonstrated that
σ_{d}
and
σ_{q}
converge to zero. The Lyapunov function can be defined as:
where
. Now the time derivative of (18) can be expressed as follows:
On the other hand, (16) and (17) imply that:
Also, the following equation can be derived from (3) and (20):
If the inequalities in (15) are used, substituting (20), (21), and (22) into (19) yields:
By applying the integration on both sides of (23):
or:
Then, (25) can be redefined as:
where
V_{0}(t)
≥ 0 is used. Then, the following inequalities can be derived:
which implies that
σ_{d}
,
σ_{q}
∈
L
_{2}
. Since
as shown in (23),
V
(
t
) does not increase and is upper bounded as
V
(
t
) ≤
V
(0). This entails that
σ_{d}
,
σ_{q}
∈
L
_{∞}
, and
ξ_{di}
,
ξ_{qi}
∈
L
_{∞}
. Hence, it can be concluded by
[21]
,
[24]
,
[25]
, and
[26]
that the closedloop system is stable.
Fig. 2
shows a block diagram of the proposed FASVC. In this figure, the load current information is estimated through a conventional load current observer
[27]
(KalmanBucy optimal observer) to reduce the number of current sensors and enhance system reliability. Therefore, the load currents (
i_{Ld}
,
i_{Lq}
) in (2) are substituted with their estimated values (
), respectively.
Block diagram of the proposed FASVC.
Remark 1:
This remark discusses the selection process of the controller gains. In this paper, for achieving a fast convergence and a rapid transient response, the adaptive gains are tuned to large values. According to (16), these adaptive gains are inversely proportional to
λ_{di}
and
λ_{qi}
. As a result, smaller values of
λ_{di}
and
λ_{qi}
can result in larger values of the adaptive gains and vice versa. On the other hand, the sliding surfaces (
σ_{d}
and
σ_{q}
) are further defined to obtain the slidingmode feedback control terms (
u_{smd}
and
u_{smq}
) given in (17), and these feedback control terms can be regarded as a PD controller. In this context, the control parameters
γ_{d}
,
γ_{q}
,
τ_{d}
, and
τ_{q}
can be designed based on the tuning rule of
[22]
. As a final point, all of the control parameters (
γ_{d}
,
γ_{q}
,
τ_{d}
,
τ_{q}
,
λ_{di}
, and
λ_{qi}
) can be tuned according to the following steps: 1) Tune the parameters (
γ_{d}
,
γ_{q}
,
τ_{d}
, and
τ_{q}
) based on the tuning rule of
[22]
; 2) Set quite large values for
λ_{di}
and
λ_{qi}
; 3) Reduce
λ_{di}
and
λ_{qi}
by a small amount; 4) End the tuning process if the present control parameters give satisfactory transient and steadystate performances, otherwise go back to
Step 3
.
Remark 2:
As described in (16) and (17), the proposed FASVC consists of two control terms: a fuzzy adaptive compensation control term (
u_{fad}
,
u_{faq}
) and a slidingmode feedback control term (
u_{smd}
,
u_{smq}
). The first term takes parameter uncertainties into account. Meanwhile, the other term stabilizes the error dynamics of the system. Therefore, the proposed control scheme can achieve good performance in the existence of the parameter uncertainties.
IV. EXPERIMENTAL VALIDATION
 A. Prototype Overview
To verify the performance of the proposed control scheme, a prototype 1 kVA threephase UPS inverter system with a TMS320F28335 DSP is tested. The parameters of the threephase UPS inverter are listed in
Table I
. Note that the values of the
LC
output filter have a cutoff frequency of 620 Hz. Normally, larger values of the filter elements (
L_{f}
and
C_{f}
) can realize better filter performance. On the other hand, large values of
L_{f}
and
C_{f}
can increase the system cost and volume. In addition, a large current flows into
C_{f}
even under no load. Therefore, selecting the
LC
output filter parameters always follows a tradeoff between their pros and cons. The guidelines for choosing an
LC
output filter for the pulse width modulation inverters are available in
[28]
.
PARAMETERS OF A THREEPHASE UPS INVERTER
PARAMETERS OF A THREEPHASE UPS INVERTER
A complete block diagram of a threephase UPS inverter system with the proposed FASVC is shown in
Fig. 3
. In this figure, both the inverter currents (
i_{iabc}
) and load voltages (
v_{Labc}
) in the stationary
abc
reference frame are sensed, and then transformed to values in the synchronously rotating
dq
reference frame by Park’s transformation (
v_{Ldq}
,
i_{idq}
). These values are first used at the conventional load current observer. After that, the estimated load currents (
) and the reference load voltages (
v_{Ld}
^{*}
,
v_{Lq}
^{*}
) are injected into the proposed FASVC. Then, after the
dq
axis voltage control inputs (
v_{ind}
,
v_{inq}
) are converted to quantities (
v_{inα}
,
v_{inβ}
) in the stationary
αβ
reference frame using an inverse Park’s transformation. Six gate pulses are generated to drive the threephase UPS inverter by using the space vector pulsewidth modulation (SVPWM) technique with sampling and switching frequencies of 5 kHz.
Overall block diagram of a prototype 1 kVA UPS inverter system.
 B. Fuzzy Rules and Controller Gains
In order to create the fuzzy models
η_{d}
and
η_{q}
given in (16), the total numbers of fuzzy rules for the four state variables (
x_{1}
,
x_{2}
,
x_{3}
, and
x_{4}
) are optimized as:
where
F
denotes the fuzzy sets and
m
is the number of state varaibles.
The fuzzy sets are characterized by two linguistic terms (negative
N
and positive
P
) for each state variable and designed in the form of the following membership functions:

gN1(x1) =e(x1+ 160)2/(320)2,gP1(x1) =e(x1 160)2/(320)2

gN2(x2) =e(x2+ 5)2/(10)2,gP2(x2) =e(x3 5)2/(10)2

gN3(x3) =e(x3+ 6)2/(12)2,gP3(x3) =e(x3 6)2/(12)2

gN4(x4) =e(x4+ 2)2/(4)2,gP4(x4) =e(x4 2)2/4)2
where the Gaussian functions are utilized as membership functions due to their simplicity. In addition, the constant values (160, 5, 6, and 2) are chosen by considering the constraints of each state variable with a small additional tolerance.
Briefly, the sixteen fuzzy rules for
η_{d}
are:
r_{1}
: IF
x_{1}
is
N_{1}
and
x_{2}
is
N_{2}
and
x_{3}
is
N_{3}
and
x_{4}
is
N_{4}
, THEN
η_{d}
is
ξ_{d1}
.
r_{2}
: IF
x_{1}
is
N_{1}
and
x_{2}
is
N_{2}
and
x_{3}
is
N_{3}
and
x_{4}
is
P_{4}
, THEN
η_{d}
is
ξ_{d2}
.
r_{3}
: IF
x_{1}
is
N_{1}
and
x_{2}
is
N_{2}
and
x_{3}
is
P_{3}
and
x_{4}
is
N_{4}
, THEN
η_{d}
is
ξ_{d3}
.
r_{16}
: IF
x_{1}
is
P_{1}
and
x_{2}
is
P_{2}
and
x_{3}
is
P_{3}
and
x_{4}
is
P_{4}
, THEN
η_{d}
is
ξ_{d16}
.
In addition, the sixteen fuzzy rules for
η_{q}
are:
r_{1}
: IF
x_{1}
is
N_{1}
and
x_{2}
is
N_{2}
and
x_{3}
is
N_{3}
and
x_{4}
is
N_{4}
, THEN
η_{q}
is
ξ_{q1}
.
r_{2}
: IF
x_{1}
is
N_{1}
and
x_{2}
is
N_{2}
and
x_{3}
is
N_{3}
and
x_{4}
is
P_{4}
, THEN
η_{q}
is
ξ_{q2}
.
r_{3}
: IF
x_{1}
is
N_{1}
and
x_{2}
is
N_{2}
and
x_{3}
is
P_{3}
and
x_{4}
is
N_{4}
, THEN
η_{q}
is
ξ_{q3}
.
r_{16}
: IF
x_{1}
is
P_{1}
and
x_{2}
is
P_{2}
and
x_{3}
is
P_{3}
and
x_{4}
is
P_{4}
, THEN
η_{q}
is
ξ_{q16}
.
Finally, the controller gains are tuned via extensive simulation studies based on
Remark 1
as:
γ_{d}
=
γ_{q}
= 130,
τ_{d}
=
τ_{q}
= 15,
ε_{d}
=
ε_{q}
= 70, and
λ_{di}
=
λ_{qi}
= 55x10
^{5}
.
 C. Comparative Experimental Results
Experimental results are demonstrated under two scenarios to fully highlight the transient and steadystate performances of the proposed FASVC as compared to the conventional SMC.
Scenario 1
shows the transientstate response and the steadystate response when a threephase resistive load with a 40 Ω resistance is instantaneously applied, i.e., no load to full load.
Scenario 2
reveals the steadystate response when a threephase diode rectifier is applied to the load terminals, which has the following
RLC
values:
R_{L}
= 90 Ω,
L_{L}
= 10 mH, and
C_{L}
= 60 μF. Meanwhile,
Scenario 3
presents the performance at the steadystate when an unbalanced resistive load is connected to the inverter output terminals, i.e., only phase
c
is opened. In this paper, 30% reductions in both
L_{f}
and
C_{f}
are assumed as the parameter uncertainties under each scenario to clearly demonstrate the transient and steadystate performances of the proposed FASVC and the conventional SMC.
Figs. 4
and
5
demonstrate the relative experimental results of both the proposed and the conventional control strategies under the three scenarios mentioned above. Both figures illustrate the waveforms of the load phase voltages (
v_{La}
,
v_{Lb}
,
v_{Lc}
), inverter phase currents (
i_{ia}
,
i_{ib}
,
i_{ic}
), and estimated load phase currents (
). Note that only (
i_{ia}
and
) for
Scenarios 1&2
and (
i_{ia}
,
i_{ib}
,
i_{ic}
and
)for
Scenario 3
are exposed. Based on
Figs. 4
and
5
, the experimental results can be described as follows:
Figs. 4
(a) and
5
(a) show the dynamic responses for
Scenario 1
.
Fig. 4
(a) depicts that the load voltage waveforms are slightly distorted and recovered to the steadystate within 0.5 ms. Meanwhile,
Fig. 5
(a) shows that it takes 1.2 ms for the load voltage waveforms to be restored to the steadystate. In
Figs. 4
(b) and
5
(b), the steadystate performances under
Scenario 2
are depicted. More specifically, it can be seen that the proposed FASVC has pure sinusoidal load voltage waveforms with a smaller THD (1.08%) than the conventional SMC (2.83%).
Figs. 4
(c) and
5
(c) elaborate the responses under
Scenario 3
at the steadystate. The load voltage waveforms presented in
Fig. 4
(c) attain a 0.47% lower THD value and reflect a sinusoidal behavior with 1.32% less steadystate error when compared with the voltage waveforms shown in
Fig. 5
(c).
Table II
summarizes the THDs and steadystate rms errors of both the proposed FASVC and the conventional SMC after each scenario reaches the steadystate condition. It can be seen in
Table II
that under all three scenarios, the THDs and steadystate errors of the proposed FASVC (i.e., about 1.10% and 0.50%, respectively) are significantly improved when compared to the conventional SMC (i.e., about 2.90% and 2.30%, respectively).
Experimental results of the proposed FASVC with –30% parameter uncertainties in L_{f} and C_{f}. (a) Sudden load disturbance (i.e., 0% to 100%). (b) Nonlinear load (i.e., Crest factor = 1.62). (c) Unbalanced load (i.e., Phase c open).
STEADYSTATE PERFORMANCES OF COMPARATIVE EXPERIMENTAL RESULTS
STEADYSTATE PERFORMANCES OF COMPARATIVE EXPERIMENTAL RESULTS
Experimental results of the conventional SMC with –30% parameter uncertainties in L_{f} and C_{f}. (a) Sudden load disturbance (i.e., 0% to 100%). (b) Nonlinear load (i.e., Crest factor = 1.62). (c) Unbalanced load (i.e., Phase c open).
V. CONCLUSIONS
In this paper, a combined fuzzy adaptive slidingmode voltage controller (FASVC) was proposed for a threephase UPS inverter. The proposed FASVC was insensitive to parameter uncertainties due to the use of a fuzzy adaptive compensation control term. In addition, the error dynamics of the system were stabilized by a slidingmode feedback control term. The stability of the proposed method was analytically proven by the Lyapunov theory. To evaluate the performance of the proposed FASVC, a prototype 1 kVA threephase UPS inverter testbed with a TMS320F28335 DSP was constructed and tested. Then, the outstanding performances (i.e., quicker voltage recovery time after a step load change, reduced THD under a nonlinear load, and smaller steadystate error for an unbalanced load) of the proposed control technique were verified through a comparison with the results obtained from a conventional SMC under three different load scenarios, and in the presence of parameter uncertainties.
Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2012R1A2A2A01045312).
BIO
Khawar Naheem received his B.S. degree in Electrical Power Engineering from the Comsats Institute of Information Technology (CIIT), Abbottabad, Pakistan, in 2011. He is currently pursuing his M.S. degree in the Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea. His current research interests are the control of distributed generation systems using renewable energy sources, electric vehicles (EVs), micro/smartgrid systems, and DSPbased electric machine drives.
YoungSik Choi received his B.S. degree in Electrical Engineering from Dongguk University, Seoul, Korea in 2009. He is presently pursuing his Ph.D. degree in the Division of Electronics and Electrical Engineering, Dongguk University. His current research interests include power conversion systems and drives for electric vehicles (EVs).
Francis Mwasilu received his B.S. degree in Electrical Engineering from the University of Dar es Salaam, Dar es Salaam, Tanzania, in 2008. From 2009 to 2011, he worked as a Utilities Engineer at JTITanzania. He is presently pursuing his Ph.D. degree in the Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea. His current research interests include distributed generation systems, electric vehicles (EVs), and renewable energy sources integration in modern power systems.
Han Ho Choi received his B.S. degree in Control and Instrumentation Engineering from Seoul National University (SNU), Seoul, Korea, in 1988, and his M.S. and Ph.D. degrees in Electrical Engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 1990 and 1994, respectively. He is currently with the Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea. His current research interests include control theory and its application to real world problems.
JinWoo Jung received his B.S. and M.S. degrees in Electrical Engineering from Hanyang University, Seoul, Korea, in 1991 and 1997, respectively. He received his Ph.D. degree in Electrical and Computer Engineering from The Ohio State University, Columbus, Ohio, USA, in 2005. From 1997 to 2000, he was with the Home Appliance Research Laboratory, LG Electronics Co., Ltd., Seoul, Korea. From 2005 to 2008, he worked at the R&D Center and with the PDP Development Team, Samsung SDI Co., Ltd., Korea, as a Senior Engineer. Since 2008, he has been an Associate Professor with the Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea. His current research interests include DSPbased electric machine drives, distributed generation systems using renewable energy sources, and power conversion systems and drives for electric vehicles (EVs).
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