Ứng dụng phương pháp điều khiển moment trực tiếp cho bộ biến đổi ma trận...

Hội nghị toàn quốc về Điều khiển và Tự động hoá - VCCA-2011

VCCA-2011

Ứng dụng phương pháp điều khiển moment trực tiếp cho bộ biến đổi ma

trận điều khiển động cơ không đồng bộ dùng logic mờ

Direct torque control for matrix converter fed induction motor drive using

fuzzy logic controller

Nguyen Phuong Duy1, Huynh Trung Nam

2, Huynh Thai Hoang

3, Nguyen Van Nho

4

Department of Electrical and Electronics Engineering

Ho Chi Minh City University of Technology e-Mail:

[email protected],

[email protected],

[email protected],

[email protected]

Tóm tắt Hiện nay, các bộ biến đổi ma trận đang nhận được sự

quan tâm rộng rãi với những ưu điểm lớn so với các

bộ biến đổi có trung gian một chiều truyền thống. Bài

báo này trình bày kỹ thuật điều khiển moment trực

tiếp ứng dụng cho bộ biến đổi ma trận. Các ưu điểm

của phương pháp moment trực tiếp và bộ biến đổi ma

trận sẽ được kết hợp để tạo ra một hệ thống có đáp

ứng nhanh và chất lượng tốt. Bộ điều khiển PI mờ

cũng được sử dụng để cải thiện chất lượng hệ thống

khi hoạt động ở tốc độ thấp cũng như khi thay đổi

moment yêu cầu. Các kết quả thực nghiệm sẽ được

phân tích để kiểm chứng ưu điểm của hệ thống này.

Abstract Matrix converter has recently attracted much concern

because of its advantages over the traditional DC link

converter. This paper presents a control method for

Matrix Converter fed induction motor drive system by

applying Direct Torque Control (DTC) technique. The

advantages of matrix converter and DTC are

combined to make a fast response and high quality

system. A fuzzy PI controller in which proportional

and integral gains can be adjusted by the fuzzy rules

is also proposed to improve the system performance

at low speed as well as under changes of torque

command. The significant experimental results will be

analyzed to verify the advantages of the proposed

strategy.

Symbol Symbol Meaning

s rψ , ψ Stator and rotor flux linkage space

vectors in stationary reference frame

s su , i Stator voltage and current space vector

Lm Mutual inductance

Ls, Lr Stator and rotor self-inductance

Te Electro-magnetic Torque

ρs, ρr Stator vector and rotor vector„s angle

p Number of poles

Acronym DTC Direct Torque Control

FOC Field Oriented Control

MC Matrix Converter

SVM Space Vector Modulation

DSP Digital Signal Processor

FPGA Field Programmable Gate Array

VSI Voltage Source Inverter

LPF Low Pass Filter

1. Introduction Three phase Matrix Converter has been recognized to

have many advantages such as providing bidirectional

power flow, sinusoidal input/output waveforms and

controllable input power factor [1-2]. Furthermore, it

allows a compact design due to the lack of DC-link

capacitors for energy storage. Recently, the invention

of four-step commutation has dramatically enhanced

the feasibility of matrix converter topology in

industrial applications. It fulfills all requirements of

safe operation and high quality. Since the Direct

Torque Control (DTC) method has been proposed in

the middle of 1980‟s, it became one of the new quick-

torque-response and high efficiency control strategy

for induction motor [5]. Comparing to the Field

Oriented Control (FOC) technique, DTC requires no

coordinate transformation and provides faster

dynamic response. More importantly, while FOC is

highly affected by the parameter changes in the

machine, the DTC strategy uses only the stator

resistance in its calculations, so less sensitive to the

machine‟s parameter changes [7]. However, because

of the simplicity in choosing the switching states, it

causes sluggish system response during startup or

when loads and reference speed change as well as the

large torque ripple. Many strategies have been

proposed to deal with these problems such as:

modified DTC using space vector modulation and a

deadbeat control, twelve sectors modified DTC or

multilevel inverters, etc. Among them, a type of

controller that allows the control rules to be adjusted

according to the value of the inputs is a fuzzy logic

controller. Such controller is often been found to be

superior to conventional controllers especially when

some information is uncertain [8]. In general, fuzzy

logic control is one of the simplest and easiest

designs, yet still improves the dynamic response

significantly. This paper presents a PI fuzzy logic

controller for DTC matrix converter fed induction

motor drive system.

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mailto:[email protected]

mailto:htnam@

mailto:hthoang@

mailto:nvnho@


VCCA-2011

2. Matrix Converter Matrix Converter (MC) is an advanced AC/AC

converter which eliminates DC link as in traditional

indirect converters. It consists of nine bidirectional

switches that allow any output phase to be linked to

any input phase, as shown in Fig. 1.

Va

Vb

Vc

M

Load

SAa

SAb

SAc

SBa

SBb

SBc

SCa

SCb

SCcLC FILTER

Bidirectional switch

IGBT

A

B

C

Figure 1: Basic scheme of Matrix Converter There are two key rules in controlling commutation

between switches of MC: 1) The input phases should

never be short-circuited; 2) The output phases should

never be open-circuited. Based on Space Vector

Modulation strategy, the following equations of line-

to-neural output voltage vector and input current

vector can be assumed:

ojω (t)j2π/3 j4π/3

o oA oB oC o

2v = v +e v +e v =v (t)e

3 (1)

ijω (t)j2π/3 j4π/3

i ia ib ic i

2i = i +e i +e i =i (t)e

3 (2)

Among 27 available switching configurations which

satisfy two above rules, only 21 switching

configurations belonged to “active group” (two output

phases connected to the same input phase) and “zero

group” (three output phases connected to the same

input phase) are useful (Table 1).

Safe commutation is the important technique in MC

because it requires a high accurate calculation in μs as

well as a synchronous and continuous pulse

generation. Using FPGA and four-step commutation

method can meet all those constraints. The

relationship between input and output factors of MC

can be described as below equations:

o iV (t)=M(t).V (t) (3)

T

i oI (t)=M .I (t) (4)

Aa Ab Ac

AaBa Bb Bc Aa

sampling

Ca Cb Cc

M M Mt

M(t)= M M M ; M =T

M M M

MAa: duty cycle of switch SAa.

tAa: period of time that switch SAa is ON.

±1;±2;±3

±7;±8;±9

±4;±5;±6

I

IIIII

IV

V VI

voωo

a)

±1;±4;±7

±2;±5;±8

±3;±6;±9

I

IIIII

IV

V VI

iiωi

b)

Figure 2: Line-to-neural output voltage (a) and input

current (b) space vector.

Switching

configuration A B C vo ωo ii ωi

+1 a b b 2/3vab 0 A2/ 3i -π/6

-1 b a a -2/3vab 0 A-2/ 3i -π/6

+2 b c c 2/3vbc 0 A2/ 3i π/2

-2 c b b -2/3vbc 0 A-2/ 3i π/2

+3 c a a 2/3vca 0 A2/ 3i 7π/6

-3 a c c -2/3vca 0 A-2/ 3i 7π/6

+4 b a b 2/3vab 2π/3 B2/ 3i -π/6

-4 a b a -2/3vab 2π/3 B-2/ 3i -π/6

+5 b c b 2/3vbc 2π/3 B2/ 3i π/2

-5 c b c -2/3vbc 2π/3 B-2/ 3i π/2

+6 a c a 2/3vca 2π/3 B2/ 3i 7π/6

-6 c a c -2/3vca 2π/3 B-2/ 3i 7π/6

+7 b b a 2/3vab 4π/3 C2/ 3i -π/6

-7 a a b -2/3vab 4π/3 C-2/ 3i -π/6

+8 c c b 2/3vbc 4π/3 C2/ 3i π/2

-8 b b c -2/3vbc 4π/3 C-2/ 3i π/2

+9 a a c 2/3vca 4π/3 C2/ 3i 7π/6

-9 c c a -2/3vca 4π/3 C-2/ 3i 7π/6

0a a a a 0 - 0 -

0b b b b 0 - 0 -

0c c c c 0 - 0 -

Table 1: Switching configurations of the MC used in DTC

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3. Conventional DTC The DTC method performs independently control of

the stator flux and electromagnetic torque at the same

time, which is also known as decoupled control. The

main purpose of this control method is to minimize

the torque and flux errors to zero by using a pair of

hysteresis comparators whose outputs, along with the

position of stator flux vector (sector), will determine

the best inverter voltage vector should be applied for

VSI. In every cycle period, a proper inverter voltage

vector is selected to maintain the estimated flux and

torque within the limits of two hysteresis bands. The

overview of conventional DTC scheme is shown in

Fig. 3.

VOLTAGE

SOURCE

INVERTER

(VSI)

IM

VECTOR

SLECTION

(Switch

configuration)

Sa

Sb

Sc

Flux_ref

Torque_ref

FLUX &

TORQUE

ESTIMATOR

--

--

CTe

Cψ+

+θψ

V1

V2V3

V4

V5 V6

I

IIIII

IV

V VI

is

vsFlux_est

Torque_est

load

Vdc/2

Vdc/2

SA SB SC

VSI

Vdc

Figure 3: The conventional DTC schematic

- Direct flux control:

The stator flux linkage in stationary frame can be

written as:

s s s sψ = u -R i dt (5)

In induction machines, impact of inductance is greater

than the one of impedance. Stator resistance (Rs) of

most high power motors is even tiny (in mΩ).

Neglecting the voltage drop on stator resistance and

calculating in a short period ∆t, equation (5) becomes:

s sΔψ =u Δt (6)

Equation (6) shows that the magnitude and orientation

of the stator flux can be control directly by selecting

appropriate voltage vector.

Sector of

stator flux

vector

Increase ↑ Decrease ↓

k Vk, Vk+1, Vk-1 Vk+2, Vk-2, Vk+3

Vk, Vk+3 are generally avoided because of their quickly

affection the flux.

V0, V7 are used to keep stator flux constant.

- Direct torque control:

The electromagnetic torque can be written as:

'm

e r s s r'

r s

L3T = p ψ . ψ sin(ρ -ρ )

2 L L (7)

The rotor time constant is usually larger than the

stator one – in other words, the rotor flux changes

more slowly than the stator flux does. Actually, the

rotor flux can be assumed constant. As long as the

stator flux modulus is kept constant, then the

electromagnetic torque can be controlled easily by

changing the angle γ = ρs – ρr.

γ

ψs

ψr

α

β

γ

ψs

ψrα

β

t = t1 t = t1 + ∆t

Vk+1, Vk+2

γ

ψs

ψr

α

β

γ

ψs

ψrα

β

t = t1 t = t1 + ∆t

Vk-1, Vk-2, V0, V7

Figure 4: The affection of VSI vector to torque

Three-level hysteresis comparator is employed

because the machine may operate in motoring mode

as well as braking mode. DTC allows induction

machine to operate in all four-quadrant of its torque-

speed plane.

Sector of stator

flux vector Increase ↑ Decrease ↓

k Vk+1, Vk+2 Vk-1, Vk-2, V0, V7

Sector

of Ψs

CΨ = -1 CΨ = 1

CTe

=-1

CTe

=0

CTe

=1

CTe

=-1

CTe

=0

CTe

=1

I V2 V7 V6 V3 V0 V5

II V3 V0 V1 V4 V7 V6

III V4 V7 V2 V5 V0 V1

IV V5 V0 V3 V6 V7 V2

V V6 V7 V4 V1 V0 V3

VI V1 V0 V5 V2 V7 V4

Table 2: Looking-up table for selecting VSI output vector

- Flux and torque estimation:

In order to exactly calculate the stator flux and torque

errors, an accurate estimator of stator flux is

necessary. Most of the stator flux calculation is based

on voltage model, current model or the combination

of both models. Among those, the voltage model is

the most popular one because it only requires the

stator resistance (Rs) for its calculation (equation 5).

However, using equation 5 shows some troubles

referred as the integration drift due to appearance of a

DC component in measured motor back emf (Electro

Motive Force). The DC offset can also be generated

when the feedback signal of stator voltage or current

is not balanced sine wave because of sensor circuits‟

inaccuracy. These problems can be resolved by

replacing the pure integrator (1/s) with a first-order

low pass filter LPF (G/(1+Ts)). In fact, LPF does not

still give accurate result in the high frequency range

of motor (lager than cut-off frequency of LPF)

because it will generate errors in magnitude and phase

angle.

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There were many solutions given by many researchers

in years to overcome this limit. The most effective

one is of Wu and Hu [9]. They added a feedback

compensation signal and an amplitude limiter to the

traditional integrator as the block diagram below:

1

G

sT

1

1 sT

Vs – is*Rs

+

+

Limitter

Ψs

Ψref

Figure 5: The advanced flux estimation diagram

4. DTC For Matrix Converter The DTC for MC was developed from the

conventional DTC for VSI. Based on the optimum

inverter vector which is selected for VSI and some

other parameters, the corresponding switching pattern

will be determined for MC.

Whatever VSI output vector is selected in the

conventional DTC, there are always six switching

configurations that can be applied to MC to generate

the same output vector (Fig. 2 a). For example,

switching configurations ±1; ±2; ±3 can be applied to

MC to generate vector V1, V4, similarly for ±4, ±5, ±6

and V2, V5; ±7, ±8, ±9 and V3, V6. Actually, at any

instance, the magnitude and direction of MC output

vectors depends on the position of the input line-to-

neural voltage vectors ei (Fig. 2 b). Once the

position of ei is determined, then only two among six

switching configurations, which have the same sector

as ei does in its space vector, are acceptable. For

instance, if it is assumed that the VSI output vector is

V1 and ei is being in sector I, it can be inferred that

switching configuration +1 or -3 will be used. The

average value of sin(ωi) (ωi is the angle between the

input current vector and the corresponding input line-

to-neutral voltage vector) is employed as the third

parameter to determined one final switching

configuration which causes the less value of sin(ωi)

than other does. The purpose of controlling isin(ω )

is to improve on the input power factor so its

proximity to zero should be achieved. Value of

isin(ω ) is obtained by applying LPF to its

instantaneous estimated value and it is also controlled

via two-level hysteresis comparator with i refsin(ω )

= 0.

-1

1

Csin(wi)

<sin(wi)>

Figure 6: Hysteresis comparator of isin(ω )

Table 3: Optimal switching configurations for DTC-based

Matrix Converter

5. Fuzzy PI Controller The DTC-based MC still has some disadvantages

including sluggish response during startup and under

changes of torque command. A fuzzy PI controller is

proposed as a possible solution for this problem. It is

well know that fuzzy logic control consists of four

elements: a rule-based, an inference mechanism, a

fuzzification interface and a defuzzification interface.

There are two input variables and two output

variables for the fuzzy PI controller as shown in Fig.

7. The inputs include speed error and change of speed

error while the outputs are KP and KI factor of the PI

regulator. Fig. 8 (a) and (b) shows membership

functions of input variables E(t) and dE(t)

respectively, which are with conventional triangular

shapes and with 50% overlapping. Each membership

is divided into five fuzzy sets: NB (negative big), NS

(negative small), Z (zero), PS (positive small), PB

(positive big). Fig. 8 (c) and (d) shows membership

functions of output variables KP and KI. Each

membership function is also assigned with five fuzzy

sets, which are VS (very small), S (small), M

(medium), B (big) and VB (very big).

Fuzzification

de/dt

Inference

engine

Rule-based

Defuzzification PI regulator

E(t) = Speed error

dE(t)

KP

KI

Torque ref

Figure 7: Block diagram of a fuzzy PI controller

Sector

of ei I II III IV V VI

isin(ω )C

+1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1

V

S

I

O

U

T

P

U

T

V

E

C

T

O

R

V1 -3 +1 +2 -3 -1 +2 +3 -1 -2 +3 +1 -2

V2 +9 -7 -8 +9 +7 -8 -9 +7 +8 -9 -7 +8

V3 -6 +4 +5 -6 -4 +5 +6 -4 -5 +6 +4 -5

V4 +3 -1 -2 +3 +1 -2 -3 +1 +2 -3 -1 +2

V5 -9 +7 +8 -9 -7 +8 +9 -7 -8 +9 +7 -8

V6 +6 -4 -5 +6 +4 -5 -6 +4 +5 -6 -4 +5

V0,7 Select zero configuration which causes the

minimum number of commutation.

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NB NS Z PS PB

-50 -25 0 25 50

µ[E(t)]

E(t) a)

NB NS Z PS PB

-20 -10 0 10 20

µ[dE(t)]

dE(t) b)

VS S M B VB

0 0.25 0.5 0.75 1

µ(Kp)

Kp c)

VS S M B VB

0 0.1 0.2 0.3 0.4

µ(Ki)

Ki d)

Figure 8: Membership functions of input variables (a,b) and

output variables (c,d)

There are two rule-based tables for two outputs

variables KP and KI, each consists of 25 linguistic

rules, as shown in Table 4, and gives the changes of

the output of fuzzy logic controller in terms of two

inputs. Those rules are established using literature and

our experiences with the help of the simulation on

computers. In this paper, the inference method used is

Mamdani‟s procedure based on max-min decision and

the widely used Center of Area (COA) method is

employed for defuzzification.

KP E(t)

NB NS Z PS PB

dE(t)

NB VB B VS S B

NS VB M VS M B

Z B M VS M VB

PS B M VS B VB

PB B S VS B VB

a)

KI E(t)

NB NS Z PS PB

dE(t)

NB VB B VS S B

NS VB M VS M B

Z B M VS M VB

PS B M VS B VB

PB B S VS B VB

b)

Table 4: Linguistic rule base for output KP (a) and KI (b)

By Table 4, some of the rules are interpreted:

If E(t)=NB and dE(t)=NB Then KP=VB and KI=VB.

Here, both the speed error and the change of error are

very big. Therefore, we need large KP and KI to

achieve a fast response.

If E(t)=Z and dE(t)= NS Then KP=VS and KI=VS.

Now, the speed error is very small or almost zero and

changes slightly. We set the KP and KI small to avoid

large overshoot.

Both the Table 4 (a) and (b) are not absolutely

symmetric because of the non-linear response of the

induction motor.

6. Experiment Hardware and Results To confirm the validity of the proposed method, the

experiments are carried out. The block diagram of the

experiment is shown in Fig. 10. The system consists

of a 3-phase induction motor fed by a matrix

converter. The motor is mechanically coupled with a

DC generator which is used as a changeable load of

the motor. The control system consists of a 32-bit

DSP (TMS320F28335) and Xilinx FPGA. The clamp

circuit plays an important role in protecting IGBTs

from harmful voltage spikes (just in case). The input

voltage and output current are measured by 12-bit

A/D converter which is on-board DSP. The cycle

period is 50 µs. The four-step commutation time is

carried out within 2µs.

3 phases

source

Input

Filter

Bidirectional

switch Module

DC Suppply

+15V/-5VDriver circuit

KIT FPGA

Spartan 3E

DSP

TMS320F28335

IM

EncoderVoltage

Sensor Circuit

Clamp

Circuit

RS232

Current

Sensor Circuit

Figure 10: The experimental block diagram of a DTC-based

matrix converter

Fig. 11 (a) and (b) show the experimental hardware

setup of power and control circuits, and induction

motor and load, respectively. The induction motor is

of 380V, 50Hz, 2 poles, 2.2KW whose stator

resistance values is RS=3Ω. Fig. 12 explains the block

diagram in implementing the proposed method.

a)

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b)

Figure 11: The experimental set (a) matrix converter and

control circuit (b) induction motor and DC generator (acted

as load)

MATRIX

CONVERTER

VSI

VECTOR

SLECTION

Flux_ref

Torque_ref

FLUX &

TORQUE

ESTIMATOR

--

--

CTe

Cψ+

+

Flux_est

Torque_est

load

Fuzzy PI Controller

ω (rpm)IM

io

vo

io

vi

θψs

DTC-Based MC

VECTOR

SLECTION

i sin(ω )

Calculationvi

oi

ii io

θvi

Csin(ωi)

Set speed

Figure 12: The whole block diagram in implementing the

DTC-Based Matrix Converter

a)

b)

Figure 13: Line-line output voltage (a) and output current

with fo = 25Hz; fi = 50Hz; R-L load (R = 11Ω; L= 40mH)

a)

b)

Figure 14: Line-line output voltage (a) and output current

with fo = 75Hz; fi = 50Hz; R-L load (R = 11Ω; L= 40mH)

a)

b)

c)

Figure 15: Induction motor performance at 400rpm with

1.5Nmn: (a) Stator phase current (b) Line-line stator

voltage (c) Input line-to-neural voltage and input phase

current

Figure 16: Rotor speed at 300rpm with 1.5Nm using

Normal PI Controller

Figure 17: Rotor speed at 400rpm with 1.5Nm using Fuzzy

PI Controller

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Figure 18: Rotor speed changes from 350rpm to 450rpm to

400rpm with 1.5Nm using Fuzzy PI Controller

To make sure four-step commutation works well with

designed DTC-Based MC‟s hardware, simple

experiments with Space Vector Modulation (SVM)

method for MC should be implemented firstly as a

dry-run test. The maximum possible supply voltage is

also determined in this step to verify the maximum

power of MC. Fig. 13 and 14 show the experimental

results of the steady state performance with RL load

using Indirect-SVM method. The line-to-line output

voltage and output phase current are both smooth. The

low order harmonics and distortion which are not

desirable are almost eliminated.

Fig.15 to Fig. 18 are the results of designed DTC-

Based MC operated with induction motor as load. Fig.

15 shows the result of the steady state performance

with indcution motor at speed 400rpm. The line to

line output voltage and output phase current are

similar to those of operation with RL load. However,

there are more distortions and the phase current is not

pure sinusoidal because of the highly dynamic

response of the induction motor. It can be clearly seen

that the input current is in phase with the input line-

to-neural voltage, confirming the unity input power

factor which is one of the most significant advantages

of this proposed method.

Fig. 16 shows the speed dynamic response using

normal PI controller. According to the result, we can

see the large overshoot in transient response and the

ripple during steady state as well.

Fig. 17, 18 desmonstrate the speed dynamic response

of the DTC method using fuzzy PI controller. Fig. 17

show the dynamic response of the system at the speed

reference 400rpm while Fig. 18 shows the response of

the system when the speed reference changes

suddenly. It can be seen from the results that the fuzzy

DTC method can achieve fast response during startup

and changes of the speed reference (or load). In

addtion, with fuzzy PI controller, the speed ripple and

overshoot are much smaller than those of normal

DTC method.

7. Conclusion This paper presents the practical implementation of a

new method, using a fuzzy logic controller associated

with direct torque control method for matrix converter

fed induction motor. The experimental results suggest

that the DTC-based matrix converter has many

advantages over other control methods, yet remains

some drawbacks including slow response during

startup, torque ripple and variable switching

frequency. To deal with such problems, a fuzzy logic

controller is proposed in order to adopt fast dynamic

and high performance. Since each scheme and method

has individual advantages and limitations, rigorous

investigation is being carried out to improve the

performance.

References [1] A.Alesina, M.G.B.V., “Analysis and Design of

Optimum-Amplitude Nine – Switch Direct AC-

AC Converters.” IEEE Trans. On Power

Electronics, 4 (1989)

[2] D. Casadei, G.S., A. Tani, and L. Zari, “Matrix

Converters Modulation Strategies: A New

General Approach Based on Space-Vector

Representation of the Switch State.” IEEE

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[3] P.W. Wheeler, J.R., J.C. Claire, L. Empringham,

A. Weinstein, “Matrix Converters: A

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[4] Lixiang Wei,T.A. Lipo, “A Novel Matrix

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IEEE 2001, Industry Applications Conference,

Vol. 3

[5] Isao Takahashi and Ohmori Youichi, “High

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Induction Motor.” IEEE Trans. Industry

Application 25(2) (1989) pp57-264

[6] Kazmierkowski, G.S.B. and M.P., “Direct

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Motors – A Survey.” IEEE Trans. On Industrial

Electronics, 51(4) (2004) pp 744-758

[7] Domenico Casadei, Francesco Profumo,

Giovanni Serra, Angelo Tani, “FOC and DTC:

Two Viable Schemes for Induction Motors

Torque Control.” IEEE Trans on Power

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[8] Lee. C. C.,”Fuzzy Logic in Control Systems:

Fuzzy Logic Control.” Part 1, IEEE Trans. on

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[9] Jun Hu and Bin Wu, “New Integration

Algorithms for Estimating Motor Fluz over a

Wide Speed Change”, IEEE Trans. on Power

Electronics, Vol. 13, No. 5, Sept 1998.

Nguyen Phuong Duy was

born in Kien Giang province,

Vietnam, in 1989. He

received his Bachelor Degree

of Electrical and Electronics

Engineering from Ho Chi

Minh City University of

Technology, Vietnam, April

2011. He is currently

working as research assistant

and teaching assistant at

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VCCA-2011

Automatic Control Lab, Division of Automation and

Control, EE Dept, HCMUT. His research interests

include power electronics, intelligent control and real

time embedded systems.

Contact: [email protected]

Huynh Trung Nam was

born in Binh Phuoc

province, Vietnam, in

1988. He received his

Bachelor Degree of

Electrical and Electronics

Engineering from Ho Chi


Technology, Vietnam,

April 2011. He is currently

working as research

assistant and teaching assistant at Automatic

Manufacturing Lab, Division of Automation and

Control, EE Dept, HCMUT. His research interests

include power electronics, computer vision.


Huynh Thai Hoang was

born in Vietnam, in 1974.

He received the M.S. and

PhD. degrees in electrical

engineering from Ho Chi


Technology, Vietnam in

1999 and 2005,

respectively. Since 1996,

he has been with

Department of Electrical

and Electronics

Engineering, Hochiminh

City University of Technology, Vietnam, where he is

currently a Vice Dean. He was with University of

Haute Alsace as a post-doc fellow for one year in

2007. His research interests are in the areas of

intelligent control and computer vision.


Nguyen Van Nho was

born in Vietnam, in 1964.

He received the M.S. and

PhD. degrees in electrical

engineering from

University of West

Bohemia, Czech

Republics in 1988 and

1991, respectively. Since

1992, he has been with

Department of Electrical

and Electronics Engineering, Hochiminh City

University of Technology, Vietnam, where he is

currently an associate professor. He was with KAIST

as a post-doc fellow for six months in 2001 and a

visiting professor for a year in 2003-2004. His

research interests are in the areas of modeling and

control of ac motors, active filters and PWM

techniques.


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