TMS320F28379D microcontroller for speed control of permanent magnet direct current motor

IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 13, No. 3, September 2024, pp. 2816~2828
ISSN: 2252-8938, DOI: 10.11591/ijai.v13.i3.pp2816-2828  2816
Journal homepage: http://ijai.iaescore.com
TMS320F28379D microcontroller for speed control of
permanent magnet direct current motor
Tanawat Chalardsakul, Chotnarin Piliyasilpa, Viroch Sukontanakarn
Department of Mechatronics Engineering, Faculty of Engineering, Rajamangala University of Technology Isan, Khon Kaen, Thailand
Article Info ABSTRACT
Article history:
Received Oct 31, 2023
Revised Feb 16, 2024
Accepted Feb 29, 2024
This paper aims to study the behavior of the proportional integral derivative
(PID) and the fuzzy-based tuning PI-D controller for speed control of a
permanent magnet direct current (PMDC) motor. The proposed method used
a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to
design and real-time implement a TMS320F28379D microcontroller for
speed control of a PMDC motor. The performance of the study designed
fuzzy-based tuning PI-D and PID controllers is compared and investigated.
The fuzzy logic controller applies the controlling voltage based on motor
speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller
approach has a minimum overshoot, and minimum transient and steady state
parameters compared to the PID controller to control the speed of the motor.
The PID controllers have poorer performance due to the non-linear features
of the PMDC motor.
Keywords:
Fuzzy logic controller
MATLAB/Simulink
Microcontroller
Permanent magnet DC motor
PID controller
This is an open access article under the CC BY-SA license.
Corresponding Author:
Tanawat Chalardsakul
Faculty of Engineering, Rajamangala University of Technology Isan
150 Srichan Road, Khon Kaen, Thailand
Email: tanawat.ca@rmuti.ac.th
1. INTRODUCTION
At present, permanent magnet direct current (PMDC) motors are an electrical machine that has been
applied in many applications to drive mechanical mechanisms [1], [2]. Tasks that require speed control,
position, or torque of the mechanical load. The power supply is alternating current; the current must be rectified
to direct current first. In most robots, it is popular to use PMDC motors that are easy to control, provide high
torque, and, most importantly, use batteries as electric power feeds. The built-in speed controller, torque, and
position can use a variety of microcontroller boards and a variety of algorithms for accurate, fast, and stable
control. PMDC motors are used in various industrial applications and robots [3] to control the rotational speed.
The voltage input to the motor is controlled using a chopper control method that can control the speed and
torque well.
The proportional integral derivative (PID) controllers [4]–[6] are used for automatic process control
and robotics in industries. PID controllers are the most popular controllers in both the process and
manufacturing industries. Furthermore, according to research on PID controllers, about ninety percent (90%)
of process industries [7] employ PID as controllers. The PID controller [8] has simplicity, stability, and
robustness; it is a type of controller that is most widely applied. This popularity is a result of their robustness,
simplicity, and ease of retuning control parameters. The PID controller has been conventionally regarded as
the best controller in the absence of fundamental process knowledge.
Fuzzy logic controller [9]–[11] are the science of computing of calculations that play a greater role in
the field of research computer and can be applied in many different jobs such as medical, military, business,
and industry. The research study to understand the science of fuzzy logic and deep neural networks, which are
to be applied in various fields, is becoming more and more in demand. The computer system that has the ability

Int J Artif Intell ISSN: 2252-8938 
TMS320F28379D microcontroller for speed control of permanent magnet… (Tanawat Chalardsakul)
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to automatically adjust the system according to the environment has changed, making smarter, more
human-like decisions so that humans can solve problems that were not previously solved by using old
knowledge that was learned and applied to effectively solve problems.
The design of the MATLAB Simulink embedded coder for TMS320F28379D [12], which is the
program used for the development and control of programming algorithms by using a set of diagrams, is
ready-made in the Simulink library. By selecting the target support package, you will find a chip support library
consisting of ready-made diagrams such as analog-to-digital converter (ADC), enhanced quadrature encoder
pulse (eQEP), and enhanced pulse width modulator (ePWM). For compilation, it can be used with the composer
studio code program, also called the CCS program. By creating code at the location of the CCS program, the
real-time working part of the MATLAB/Simulink program, this CCS program will be compiled into the C
language first. It then converts the data into machine language for the controller. Digital signals from the
TMS320F28379D microprocessor board can be debugged into programs through the joint test action group
(JTAG) emulator to store data in registers without having to compile the program. There are many research
articles showing how to control the speed and position of a DC motor using a simulator. The first step is to find
the parameters using MATLAB to solve the control and display problems, and the TMS320F28379D board
with MATLAB program is used for real-time use [13].
The DC-to-DC converter, also called chopper circuits, is a circuit that is commonly used in industrial
applications and computers. A chopper circuit involves changing a DC power supply from one voltage to
another. It consists of power electronic devices such as bipolar junction transistor (BJT), silicon controlled
rectifier (SCR), insulated gate bipolar transistor (IGBT), or gate turn-off thyristor (GTO); which act as switches
controlling the duty cycle of the output waveform, making it possible to control the average value of the output
voltage of the chopper circuit helps to control the acceleration or Speed of DC electric motor to be highly
efficient, smooth and responsive to move quickly This makes the chopper circuit [14] suitable for many types
of work, such as the braking of DC electric motors. To return energy to the supply and resulting in saving
energy. The chopper may act as a source that converts the DC voltage down, or it may act as a source that
converts the DC voltage to a higher level.
The paper presents the following topics. The mathematical modeling and control objectives are
described in section 2. In section 3, the experimental study of controller systems such as PID controllers and
adaptive PI-D controllers is carried out. The fuzzy dressings [10], [11], [15] are respectively designed. In
section 4, the designed controller testing methods are applied to the PMDC motor model, along with the
experimental results [16], [17]. Finally, the results of the experiment are summarized in section 5.
2. RESEARCH METHOD
2.1. The mathematical modelling of permanent magnet direct current motor
PMDC motor uses permanent magnets located in the stator to provide the magnetic field instead of it
being created in stator windings. The equivalent circuit diagram of the PMDC motor [18] is the
electromechanical system consisting of electrical and mechanical components as shown in Figure 1.
Figure 1. The equivalent circuit diagram of the PMDC motor
When a voltage is applied to the armature winding, it creates a magnetic field in the armature winding
and interacts with the permanent magnetic field in the stator to create torque in the armature, as shown in (1).
𝑇𝑚 = 𝐾𝑡𝑖𝑎 (1)
Where 𝑇𝑚 is the developed torque in the motor, 𝐾𝑡 is the torque constant, and 𝑖𝑎 is the armature current. The
armature winding intersects with the result of the magnetic field and creates a back electromotive force (EMF)
in the armature winding, as shown in (2).
𝑒𝑎(𝑡) = 𝐾𝑏
𝑑𝜃𝑚(𝑡)
𝑑𝑡
= 𝐾𝑏𝜔𝑚 (2)

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Int J Artif Intell, Vol. 13, No. 3, September 2024: 2816-2828
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where 𝑒𝑎 is the back EMF, 𝐾𝑏 is the EMF constant, and 𝜔𝑚 is shaft angular velocity. Applied Kirchhoff’s law
of input voltage as (2):
𝑉𝑖𝑛(𝑡) = 𝑅𝑎𝑖𝑎(𝑡) + 𝐿𝑎
𝑑𝑖𝑎(𝑡)
𝑑𝑡
+ 𝐾𝑏
𝑑𝜃𝑚(𝑡)
𝑑𝑡
(3)
where 𝑅𝑎 is the armature resistance, 𝐿𝑎 is the armature inductance, 𝜃𝑚 is the motor shaft output angle, and
𝑉𝑖𝑛 is the input voltage. Taking Laplace transform in (3), given as in (4)
𝑉𝑖𝑛(𝑠) = 𝑅𝑎𝐼𝑎(𝑠) + 𝐿𝑎𝑠𝐼𝑎(𝑠) + 𝐾𝑏𝑠𝜃𝑚(𝑠) (4)
The transfer function of the PMDC motor is as (5) and (6):
𝐼𝑎(𝑠)
[𝑉𝑖𝑛(𝑠)−𝐾𝑏𝜔(𝑠)]
=
1
(𝐿𝑎𝑠+𝑅𝑎)
(5)
𝐼𝑎(𝑠) =
[𝑉𝑖𝑛(𝑠)−𝐾𝑏𝜔(𝑠)]
(𝐿𝑎𝑠+𝑅𝑎)
(6)
The mechanical mathematical model is the sum of the torques, shown as (7):
𝐾𝑡𝐼𝑎(𝑠) = (𝐽𝑚𝑠 + 𝑏𝑚)𝑠𝜃(𝑠) + 𝑇𝐿(𝑠) (7)
where 𝑇𝐿 is the load torque, 𝐽𝑚 is the inertia of the motor, and 𝑏𝑚 is the damping friction, the mechanical
component transfer function is given by (8).
𝜔𝑚(𝑠)
𝐾𝑡𝐼𝑎(𝑠)−𝑇𝐿(𝑠)
=
1
𝐽𝑚𝑠+𝑏𝑚
(8)
If 𝑇𝐿 = 0, we have
𝜔𝑚(𝑠)
𝐾𝑡𝐼𝑎(𝑠)
=
1
𝐽𝑚𝑠+𝑏𝑚
(9)
Then:
𝐾𝑡𝐼𝑎(𝑠) = (𝐽𝑚𝑠 + 𝑏𝑚)𝑠𝜃(𝑠) (10)
The relationship between the input voltage and the motor shaft output angular velocity of the PMDC motor
without a load attached is shown in (11).
𝜔𝑚(𝑠)
𝑉𝑖𝑛(𝑠)
=
𝐾𝑡
[(𝐿𝑎𝑠+𝑅𝑎)(𝐽𝑚𝑠+𝑏𝑚)+𝐾𝑡𝐾𝑏]
(11)
The simplification of the open-loop transfer function of the PMDC motor without load is shown in (12).
𝜔𝑚(𝑠)
𝑉𝑖𝑛(𝑠)
=
𝐾𝑡
[(𝑅𝑎𝐽𝑚)𝑠+(𝑅𝑎𝑏𝑚)+𝐾𝑡𝐾𝑏]
(12)
Consider in (12), the transfer function relationship between the input voltage and the rotational speed of the
motor as shown in Figure 2. Next is a reference to Table 1 shows the electrical properties of the PMDC motor
used in the experimental study. Then Table 2 explains the mechanical properties of the PMDC motor.
Figure 2. The block diagram of the PMDC motor

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Table 1. The electrical properties of PMDC motor
Parameter Value
Nominal voltage
Rate power input
No load speeds
No load currents
Nominal current
Electric resistance
Electric inductance
Back EMF constant
24 V
100 W
3500 rpm
0.55 A
3.1 A
2.1 Ω
0.035 mH
0.006 V/rpm
Table 2. The mechanical properties of PMDC motor
Parameter Value
Moment of inertia
Friction coefficient
Torque constant
0.00025 Nm/rad/s2
0.0002 Nm/rad/s
0.057 Nm/A
2.2. Fuzzy-based tuning PI-D controller structure
The PID controller cannot tune the parameters of the plant control and cannot be adjusted when the
electric motor operates in a non-linear manner. Hence, fuzzy-based tuning of PI-D is necessary to automatically
tune the PI-D parameters. The proposed controller is shown in Figure 3.
Figure 3. Block diagram of the fuzzy-based tuning PI-D controller
The output of the fuzzy-based tuning PI-D controller is given in (14)
𝑈𝑐𝑜𝑛𝑡(𝑡) = 𝐾𝑃𝑒(𝑡) + 𝐾𝐼 ∫ 𝑒(𝑡)𝑑𝑡
𝑡
0
+ 𝐾𝐷
𝑑
𝑑𝑡
𝑒(𝑡) (14)
With its Laplace transform:
𝑈𝑐𝑜𝑛(𝑠) = 𝐾𝑃 +
𝐾𝐼
𝑠
+ 𝐾𝐷𝑠 (15)
By using backward Euler methods for both the integral and derivative terms, the resulting discrete-time PID
controller is represented in (16):
𝑈𝑐𝑜𝑛(z) = KP +
KITsz
z−1
+
KDN(z−1)
(1+NTs)z−1
(16)
Details of the fuzzy logic controller [19]–[21] are shown in Figure 4, where there are two inputs to
the fuzzy inference: 𝑒 , and ∆𝑒. And two outputs: 𝐾𝑃𝑓 , and 𝐾𝐼𝑓. The parameters 𝑘1, 𝑘2, 𝛼 and 𝛽 are input/output
scaling factors. To determine the domain of each PI-D parameter [22]. The parameters of PI were defined as:
𝐾𝑃 ∈ (0,100), 𝐾𝐼 ∈ (0,1). Thus, the scales of the fuzzy interval (0, 1) as follows:
𝐾𝑃 = 𝛼𝐾𝑃𝑓 + 𝑃1 (17)
𝐾𝐼 = 𝛽𝐾𝐼𝑓 + 𝑃2 (18)
𝐾𝐷 = 𝑃3 (19)
where, 𝛼 = 0.15, 𝛽 = 40, 𝑃1, 𝑃2 and 𝑃3 is external gain input.

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Figure 4. Structure of thr fuzzy logic controller
A fuzzy logic controller has four main components fuzzification interface, inference mechanism, rule
base, and defuzzification interface. It consists of three membership functions with two inputs and one output.
Each membership function consists of two trapezoidal memberships and five triangular memberships.
Figure 5 shows the membership function of fuzzy input controllers. Figure 5(a) is the FIS editor. Figure 5(b)
is membership function of error as input. Figure 5(c) is the membership function of ang_in_error as input. The
surface viewers and rules of 𝐾𝑃 and 𝐾𝐼 are shown in Figure 6.
(a) (b)
(c)
Figure 5. Membership function of fuzzy input controllers; (a) the FIS editor, (b) membership function of
error as input, and (c) membership function of ang_in_error as input

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Figure 6. Surface viewer and rules
2.3. Chopper circuit
In the operation of the chopper, shown in the circuit diagram of the chopper in Figure 7, the circuit is
using control of the on-off period. During operation of the period, the chopper [23] is on, and the output voltage
is equal to the source voltage. When the chopper is off and the output voltage is zero. The average voltage
shown is the following:
𝑉
𝑜 = (
𝑇𝑜𝑛
(𝑇𝑜𝑛+𝑇𝑜𝑓𝑓)
)𝑉1 (20)
𝑉
𝑜 = (
𝑇𝑜𝑛
𝑇
)𝑉1 (21)
𝑉
𝑜 = 𝛼𝑉1 (22)
𝑉
𝑜 = 𝑓𝑇𝑜𝑛𝑉1 (23)
where 𝑇𝑜𝑛 is on-time, 𝑇𝑜𝑓𝑓 is off-time, 𝑇 = 𝑇𝑜𝑛 + 𝑇𝑜𝑓𝑓 is chopping period, 𝛼 is duty cycle percent, 𝑓 =
1
𝑇
is
chopping frequency, 𝑉
𝑜 is output voltage, and 𝑉1 is source voltage.

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Figure 7 shows the circuit diagram for the PMDC motor. The step-down chopper consists of IGBT
switch (M2) and a diode (DF). A DC voltage source of input voltage (V1) is connected at the input, while a
PMDC motor is connected at the output.
Figure 7. Circuit diagram of step-down chopper with PMDC motor connection
3. EXPERIMENT OF PROPOSED METHOD
3.1. Experimental platform practice
The experiment platform model [24]–[26] circuit is made up of a PMDC motor with an encoder to
detect the speed of the motor. The connection of the personal computer to the TMS320F28379D board for the
generation of pulse control to the chopper circuit drive and education practice set. When the electrical circuit
is complete, open the MATLAB program. Perform a test on the specified program as shown in Figure 8.
Figure 8. The experimental setup
The experimental model practice includes potentiometers, which allow modifying the system by
modifying the gain of the system. This manual adjusts the parameters of the potentiometer, which has four
different gain values (Kp, Ki, and Kd) as shown in Figure 9. Define input PID controller by using

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potentiometers: P1 is P-gain (0-10Vdc) convert to 0-4095 (12bit). P2 is I-Gain (0-10Vdc) convert to
0-4095(12bit), P3 is D-gain (0-1Vdc) convert to 0-4095(12bit). P5 is set point reference input, C2 is feedback
path from speed sensor from 0-4000 rpm to 0-4095(12bit).
Figure 9. Experiment lab practice set
3.2. The proportional integral derivative controller with MATLAB/simulink program
The experiment model study in only PID control [27], [28] is shown in Figure 10. The PID controllers
tune parameters using hand-tuning methods [29]. This PID controller combines proportional, integral, and
derivative controls together. The controllers are connected in parallel. The gain values of 𝐾𝑃, 𝐾𝐼, and 𝐾𝐷 depend
on the condition of the error between the input value and the output value. When tuning these parameters, the
dynamic reactivity of the system can be improved, eliminating steady-state errors, reducing overshoot, and
increasing the stability of the controlled system. The block parameters of the PID (z) controller to use a
discrete-time domain is shown in Figure 11.
Figure 10. The MATLAB/Simulink block diagram in experimental setup

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Figure 11. The block parameters PID controller in MATLAB/Simulink
3.3. Experiment of the fuzzy-based tuning PI-D controller
The working principle of fuzzy self-adjustment is that all error values are changed to adjust the
parameters of the three PID controllers in real time. So that the system has good dynamic and static
performance to desire response, minimize the settling time and rise time. The experimental cycle model in
MATLAB/Simulink is shown in Figure 12.
Figure 12. The experiment Simulink model of the fuzzy-based-tuning PI-D controller for PMDC motor
4. EXPERIMENT RESULTS AND DISCUSSION
The experimental results of both PID and fuzzy-based tuning PI-D controller were used to compare the
advantages and disadvantages of speed control for the PMDC motor. The experiment is proposed to validate the
correctness of the controller based on the model used to test that it has good motor speed control performance
using both PID and fuzzy-based tuning PI-D controller schemes. The test results can be explained as follows.
4.1. The results of PID controller for speed control of PMDC motor
The results of the experiment have been done using a PC with the Windows 10 operating system and
MATLAB/Simulink R2018a. The test results of the motor speed control circuit with a PID controller are shown

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in Figure 10. It can be seen that while changing the input reference value, it takes a very long time for the
velocity to reach the reference value, and in Figure 13, in the steady state, there is oscillation. The motor speed
is unstable, and there is vibration.
Figure 13 shows test results from Figure 10 that have been performed, producing sudden changes in
the PMDC motor. These tests have been performed by varying the potentiometer gains of 𝐾𝑃 = 5, 𝐾𝐼 = 2.8,
𝐾𝐷 = 0, and set point gain (P5). It has not been saturated in any of the experimental cases. The steady-state
errors of the dynamics system change with the same gain by the controller in Figure 13(b), Observe the
steady-state response when using a PID controller. It will be found that the rotational speed of the motor is
fluctuating and does not rotate smoothly, as shown in Figure 14. A better controller must be found to adjust
this value. As will be presented in the next chapter.
(a)
(b)
Figure 13. Experiment speed response of PMDC motor under difference speed reference input, (a) response
of motor speed after use PID controller and (b) the output gain of PID controller
4.2. The results of fuzzy based tuning PI-D controller for speed control of PMDC motor
From the results of the experimental study, when connecting the circuit and using the frame diagram
shown in Figure 12, the test results can be seen in Figure 15. The parameters of the fuzzy-based tuning PI-D
controller adjustments are automatically adjusted to meet the desired response at various speed values.
Therefore, it is found that the fuzzy-based tuning PI-D controller provides better system control performance
than the conventional PID controller. When comparing the performance of the conventional PID controller with
the customized fuzzy-based tuning PI-D controller, it was found that the fuzzy-based tuning PI-D controller

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provides better dynamic response. It has a shorter settling time, rise time, a steady state error of zero, an
increasing maximum time exceeded, and a fairly strong anti-interference ability. When considering Figure 13
shows experiment speed response of PMDC motor under difference speed reference input. Figure 13(a) is
response of motor speed after use PID Cotroller. Figure 13(b) is the output gain of PID controller. Figure 14
show the zoom out of experiment speed response of PMDC motor under steady-state response of speed reference
input condition. Figure 14(a) is the steady state response of speed motor. Figure 14(b) is the gain of controller.
Figure 15 shows experiment speed response of PMDC motor under difference speed reference input condition.
Figure 15(a) is response of motor speed after use PID controller. Figure 15(b) is the output gain of fuzzy based
tuning PI-D controller. When comparing the results of the motor speed responses of two types of controllers, it
can be seen that the fuzzy-based tuning PI-D controller is able to control the system with more stability. There
is less fluctuation in rotational speed or overshoot than with a PID controller, and reaching the set value or set
point in a shorter time can be both positive and negative. In order to make the control system able to respond to
external noise very well, this makes the control stability constant under various noises or disturbances. Table 3
summarizes the advantages and disadvantages of both controllers.
(a) (b)
Figure 14. The zoom out of experiment speed response of PMDC motor under steady-state response of speed
reference input condition: (a) the steady state response of speed motor and (b) the gain of controller
(a) (b)
Figure 15. Experiment speed response of PMDC motor under difference speed reference input condition,
(a) response of motor speed after use PID controller and (b) output gain of fuzzy based tuning PI-D controller
Table 3. The comparison between the conventional PID controller and the fuzzy based tuning PI-D controller
Parameter PID Controller The fuzzy based tuning PI-D controller
Overshoot
Settling
Transient
Rise time
Present
More
Present
Less
Not present
Less
Not present
More

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5. CONCLUSION
Results of testing motor speed control using a chopper circuit, that has a TMS320F28379D
microcontroller board as a generator for driving signals and measuring using the program MatlabSimulink in
real time to compare the controllers for controlling the speed of electric motors by using PID and the fuzzy-
based tuning PI-D controller. From the test, it was found that the control performance of the two controllers
would be compared. Both types of controllers are designed to have convergence times to steady state (setting
time) at the same time. The fuzzy-based tuning PI-D controller provides slightly better steady-state error values.
PID controllers must be adjusted to every control at the desired rpm, whereas fuzzy logic controllers require.
It has a wider range of adjustments each time and has a better response than using fuzzy logic to adjust the
parameters. The 𝐾𝑃, 𝐾𝐼, and 𝐾𝐷 gains of the PID control system are control systems that adjust the response to
changes between input and output data. The fuzzy-based tuning PI-D controller is an automatic system with a
PID controller controlling the processing of results and a fuzzy controller adjusting the value. Parameters of
PID Controller This system is a system that can adjust parameters automatically (self-tuned PID controller).
ACKNOWLEDGEMENTS
The authors like to thank Department of Mechatronics and RMUTI Khon Kaen for providing the
laboratory and experimental lab practices for this research.
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BIOGRAPHIES OF AUTHORS
Tanawat Chalardsakul is an Assistant Professor at the Field of Mechatronics
Engineering at the Rajamangala University of Technology Isan Khon Kaen Campus,
Thailand. He received M.Eng. in Electric Power System and Ph.D. in Buddhist studies from
Mahachulalongkornrajavidyalaya University. His research interests are mechanical system,
renewable energy and robot design. He can be contacted at email: tanawat.ca@rmuti.ac.th.
Chotnarin Piriyasilpa is a lecturer in Mechatronics Engineering at the
Rajamangala University of Technology Isan Khon Kaen Campus, Khon Kaen, Thailand. He
received M.Eng. in Electric Power. He has been an Assistant Professor at the Field of
Mechatronics Engineering at the Rajamangala University of Technology Isan Khon Kaen
Campus. His research interests are mechanical system, renewable energy and robot design.
He can be contacted at email: chalermchai.pi@rmuti.ac.th.
Viroch Sukontanakarn is a lecture in Mechatronics Engineering at the
Rajamangala University of Technology Isan Khon Kaen Campus, Khon Kaen, Thailand. He
received his M.Eng. in Electric Power System Management and D.Eng. in Mechatronics from
Asian Institute of Technology, in 1998 and 2011, respectively. He has been an Assistant
Professor at the Field of Mechatronics Engineering at the Rajamangala University of
Technology Isan Khon Kaen Campus, Thailand since 2002. His research interests are power
electronics, electrical power systems, microcontrollers, robotics, programmable logic
controller, and electric motor drive. He can be contacted at email: viroch.su@rmuti.ac.th.

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