INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

Page 2350

Speed Control System of Brushless DC Motor Using Fuzzy Logic PID

Controller in Automatic Guided Vehicle
Pyong-Suk Ri, Jin-Myong Hwang, Jin-Song Kim, Yong-Kyong Kim, Kum-Song Ri*

Faculty of Mechanical Science and Technology, Kim Cheak University of Technology, Kyogudong
No.60, Yonggwang street, Pyongyang 950003, Democratic People’s Republic of Korea

*Corresponding Author

DOI: https://dx.doi.org/10.51244/IJRSI.2025.1210000208

Received: 07 October 2025; Accepted: 14 October 2025; Published: 15 November 2025

ABSTRACT

Brushless DC motors are employed in wide variety of applications including aerospace, robotics, healthcare and
so on due to their advantages such as high operating life, high efficiency, high dynamic response. This paper
describes speed control system of BLDC motor in Automatic Guided Vehicle using Fuzzy PID controller. The
signal obtained from Hall-effect sensor of the motor can be analyzed to measure velocity of the robot. Fuzzy
PID controller with high speed response characteristic and robustness is designed to maintain velocity of the
robot. Thus workpieces on the robot don’t fall down under the several conditions such as setting off and stopping
of the robot and sudden load variation. Using designed FPID controller, we can calculate output voltage and
drive BLDC motor by using Pulse Width Modulation method. Several simulations using MATLAB are taken to
assessment the advantages of the proposed method. This control system reveals high speed response
characteristics and reliability in running operation especially under the varying load condit ion.

Keywords: BLDC motor, Hall-effect sensor, Automatic Guided Vehicle, Fuzzy PID, speed control, PID
controller

INTRODUCTION

It is very important task to design speed control system of BLDC motor with high performance. When the robot
sets off and stops, and the load varies, workpieces on it may fall down because of the sudden change of robot
speed. Moreover, under sudden load variations the rotation torque will increase suddenly and the flowing current
will be exceeded. If this state lasts longer than desired time or this case will be repeated, it leads to breakdown
of the motor. In order to control robot speed, the rotating speed of it should be measured ahead. The control of
BLDC motor can be classified as sensor-based control and sensor-less control. In sensor-based control, the stator
winding is excited based on rotor position which is measured using Hall-effect sensors[1]. BLDC motors often
incorporate either internal or external position sensors to measure the actual rotor position[2]. The rotating speed
of the BLDC motor can be measured using Hall-effect sensors which are placed on the stator. Each time the
permanent magnets pass the sensor, pulse signals are generated that are used in speed measurement. The more
the number of the magnets, the more the number of the generated pulse signals and improve the accuracy of the
speed measurement.

PID control is widely used not only in process control but also in BLDC motor control system. The performance
of a speed controller mainly depends on tuning of PID gains. Tuning is nothing but finding appropriate
proportional, integral, and derivative gains of PID controller to meet the desired performance[1]. Conventional
PID controller is widely used in linear control system because of its simple structure and high robustness.
However, most of processes and control system have nonlinearity and it is impossible to make exact
mathematical model of the object. Thus, high controllability cannot be obtained through only conventional PID
control scheme.

Fuzzy control is knowledge-based or rule-based control technique. The purpose of it is to describe the
experiences of the operator as fuzzy control rules and then realize operator-similar control based on inference.

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

Page 2351

Fuzzy control can be used when the mathematical model of the object is ambiguous or unknown[6]. Fuzzy
control can be applied to complicated object efficiently; while it has some drawbacks. For example, the static
error still remains because it doesn’t have integral operation. Fuzzy PID controller is used to improve static
characteristics of the fuzzy controller[3].

Conventional PID controller can exert satisfactory performance when the controlled object is stable or the output
is in normal state. But, in the case of sudden change of the object or being in the abnormal state, PID controller
cannot fulfill control task alone. Thus, Fuzzy PID controller which can tune PID coefficients based on fuzzy
logic are adopted to realize such kind of control task. For FPID is combination of fuzzy control and PID control,
so it can incorporate the advantages of them, which are good dynamic characteristic in fuzzy control and good
static characteristic in PID control, to obtain better control performance.

In this paper, we described speed control system of the BLDC motor in Automatic Guided Vehicle using Fuzzy
PID controller. The real speed of the robot is measured by using Hall-effect sensor. The error between the real
speed and reference value and the change of the error can be used to input signal of fuzzy logic controller in
order to obtain PID coefficients. MATLAB simulation results demonstrate that the system reveals faster speed
response and higher robustness when using proposed method than conventional PID.

The remainder of this paper is organized as follows: Section 2 shows related works. Section 3 describes
modelling of BLDC motor. Section 4 shows speed control system of BLDC motor using Fuzzy PID. Section 5
describes hardware implementation of the robot control system. Section 6 shows simulation results using
MATLAB. Section 7 concludes the paper.

Related Works

Chung-Wen Hung, Jhih-Han Chen, Ke-Cheng Huang[7] proposes a Hall sensor-based circuit for correcting the
errors occurring in the speed measurement of a brushless DC (BLDC) motor. Although the poles of the rotor are
always placed with a uniform angular separation, the Hall sensors may not. The angular misalignment between
the poles and the Hall sensors will cause errors in the speed measurement of the motor. They designed and
implemented a new correction circuit which can reduce measurement error below 45% using FPGA. E.A.
Ramadan, M. El-bardini, M.A. Fkirin[8] realized adaptive fuzzy speed control system of DC motor using FPGA.
They used adaptive fuzzy logic control algorithm in order to overcome nonlinearity of DC motor. Hardware was
implemented by using FPGA and verified tracking and other performances under operating condition. Akash
Varshney, Deeksha Gupta, Bharti Dwivedi[2] proposed fuzzy PID controller which can exert stable speed
response under varying load. Modelling and controller design were carried out using MATLAB and the
performance comparison between conventional PID and fuzzy PID were conducted. Kandiban, R.
Arulmozhiyal[9] proposed speed control system of BLDC motor using adaptive fuzzy PID controller and
simulated using MATLAB. This paper proves that proposed adaptive fuzzy PID controller has stable speed
response when the load varies. K. Sarojini Devi, R. Dhanasekaran, S.Muthulakshmi[10] introduced a new
method to improve speed control performance of BLDC motor using fuzzy PID controller. Here, self-tuning
fuzzy PID control technique is used to minimize overshoot and shorten setting time. Songmao Zhang, Yunliang
Wang[11] simulated speed control system of BLDC motor based on optimized fuzzy PID control algorithm.
They proved that fuzzy PID control algorithm has faster response, higher control precision and robustness than
conventional PID or fuzzy control. Devendra Somwanshia[12] used fuzzy PID and conventional PID controller
to compare control speed control performances of DC motor. They used fuzzy controller to tune PID coefficients
and simulate the system using LabVIEW.

In most of related works, there were only simulation tests about motor speed control system using MATLAB or
LabVIEW, or some kind of simple experiment device for investigation purpose. Moreover, there doesn’t exist
any research paper which describes speed control system with high speed response and robustness under
different situations such as setting off and stopping of vehicle and sudden load condition when BLDC motor is
used to drive Automatic Guided Vehicle.

This paper describes FPID control system of BLDC motor that is used to drive Automatic Guided Vehicle under
different situations, hardware and software implementation and simulation results using MATLAB.

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

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Modelling of BLDC motor

In order to configure speed control system of BLDC motor, dynamic model should be made beforehand. General
mathematical model of BLDC motor is as follows.




































































































ccbca

bcbba

acaba

e
e
e

i
i
i

dt
d

LLL
LLL
LLL

i
i
i

R
R

u
u
u

00
00
00

(1)

Here, cba uuu ,, are three phase voltages( V ), cba eee ,, are three phase back-electromotive forces( V ),

cba RRR ,, are three phase inductive resistances( ), cba iii ,, are three phase currents( A ), cba LLL ,, are three
phase self-inductances( H ), cbcabcbaacab LLLLLL ,,,, are three phase mutual-inductances( H ). For convenience
in analysis, resistances, self-inductances and mutual-inductances are same respectively.

MLLLLLLLLL cbcabcacabcba  , , RRRR cba 

Eq. (1) can be written as follows.




































































































e
e
e

i
i
i

dt
d

LMM
MLM
MML

i
i
i

R
R

u
u
u

00
00
00

(2)

For the three-phase symmetrical motor, ,0 cba iii

0 cba MiMiMi , acb MiMiMi  , bca MiMiMi  , cba MiMiMi 

Eq. (2) is presented as following equation.









































































































e
e
e

i
i
i

P
ML

ML
ML

i
i
i

R
R

u
u
u

00
00
00

(3)

where P is derivate operator with time.
dt
d

P 

aaaa ei
dt
d

MLRiu  )(

bbbb ei
dt
d

MLRiu  )(

cccc ei
dt
d

MLRiu  )(

Torque value can be expressed by back-electromotive force and current of each phase.

IKieieieT tccbbaae  /)( (4)

where  is angular velocity of BLDC motor(rad/s), eT is electromagnetic moment of motor, tK is moment
coefficient.

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

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cba iiiI 

When there is no load, electromagnetic moment of motor is expressed as:

dt
d

JBT MMe


  (5)

When there is load, eT is,

dt
d

JBTT MMLe


  (6)

where
LT is load torque( mN  ),

MB is friction coefficient of BLDCM( radsmN / ),
MJ is motor inertia

(
2mkg  ).




LLL B
dt
d

JT  (7)

where
LJ is load inertia(

2mkg  ),
LB is damping coefficient( radsmN / ).

The equation of state of motor is written as:





















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






































































































e
e
e

i
i
i

R
R

u
u
u

i
i
i

P
00

00
00

1
00

0
1

00
1

(8)

Parameters of motor are resistances, inductances of each phase and inertia and friction of motor and load. To
design PID controller, parameters of BLDCM and load should be determined. Varying parameters are

LMLM BBJJR ,,,, . These influence on speed response of BLDCM.

Speed control system of BLDCM using Fuzzy PID controller

Fuzzy logic control theory

Fuzzy controller is composed of fuzzification, rule inference and defuzzification. The error of velocity and
change of error are expressed:

actrefke  )(

)1()()(  kekeke

)()1()( kukuku 

Control knowledge or experience of an expert is expressed as a set of fuzzy IF-Then rules of the form:

IF e is iA and e is iB THEN fU is iZ (9)

where, mi ,...,3,2,1

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There are seven fuzzy sets for each linguist value {

iA ,
iB ,

iZ }. These are NB(Negative Big), NM(Negative
Medium), NS(Negative Small), ZO(Zero), PS(Positive Small), PM(Positive Medium), PB(Positive Big). And
49 fuzzy control rules are designed for two inputs and one output fuzzy system fU . A maximum of four rules
will be active at any time[8].

))()),(),(min(min(max)(
)1,(

fZBA
iij

fZ UeeU
jjj

 


(10)

))(),(()( eeU
jj BAi  

))(),(min(min(max)(
)1,(

fZi
iij

fZ UUU
j




 (11)

After that, defuzzifier converts Eq. (9) into the following expression:










n

i i

i ii
f

CU
U

)(




(12)

where, )( iU is the values of membership function (MF) for output and iC is the values of output MFs centers.
Next this result is transferred to the BLDC motor of AGV.

Fuzzy PID controller Design

Conventional PID control technique is widely used in application field. Assume that 0pk , 0ik , 0dk are initial
gain, integral coefficient and derivate coefficient. In conventional method, initial parameters are determined by
experience and they don’t change in control process. The output of conventional PID is expressed as:

dt
tde

kdttektektu dip
)(

)()()( 000   (13)

where, )()( tte ref   and )(t is current speed of BLDCM at time t . ref is speed reference value of
BLDCM, )(te is difference between current speed and reference value. As mentioned above, coefficients of
conventional PID never change all the time, therefore this control method isn’t suitable for some processes with
nonlinearity and cannot avoid influence of sudden change[16,19]. Fuzzy PID control is combination of fuzzy
logic and PID control, it reveals high control performance before specific objects with nonlinearity or parameter
uncertainty. PID coefficients after passing FPID are expressed as:













ddd

iii

ppp

kkk
kkk
kkk

(14)

In this paper, we propose fuzzy PID controller of speed control system of BLDCM, in which the error value e
and error change e are inputs and pk , ik , dk are outputs. We take the fuzzy subset of fuzzy language
variables of inputs are NB, NS, ZE, PS, PB, representing a negative large, negative small, zero, positive small
and positive large respectively. Output language variables are NB, NM, NS, ZO, PS, PM PB, representing a
negative large, negative medium, negative small, zero, positive small, positive medium and positive large
respectively. Inputs and outputs are selected Gaussian membership functions and

ek and
eck are quantized (-0.2,

0.2), (-0.05, 0.05), (-2,2) region.

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Fig.4 The membership functions of
ik

Fig.5 The membership functions of
dk

Based on knowledge and experience of an expert about control object, fuzzy control rules are made.

Table 1 Fuzzy rule table of
dk ,

dk and
dk

ec e

NB NM NS ZO PS PM PB

NB NB PB
PS

NB PB
PS

PB NB
ZB

PB NB
ZO

PB NM
ZO

ZE ZE
PS

NM NM PM
PS

NB PM
PM

NS PS
PM

PM NM
ZO

PM NM
PS

PM NM
NS

ZE PM
PS

NS PB NB
NB

PM NS
NB

NS PS
ZE

PS ZO
NS

ZO ZO
ZO

ZO PS
NS

PS PS
NM

ZO PS NS
NB

PS PS
NM

ZO ZO
NS

PS NS
NB

PS NS
NS

PS NS
PS

PS NS
ZO

PS PS NS
NB

NS PS
NM

NS PS
NS

NS PS
ZO

NS PS
ZE

PS ZO
NM

PM NS
ZO

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PM PS NS

NB
NS PM

NM
NS PS

NS
PB ZO

NM
NS PM

ZO
PM ZO

NM
PM NS

PB PB PS
NM

PB NM
PM

NS PS
ZE

PB NB
ZO

PB NB
ZE

PB NB
NS

PB NM
NB

Fuzzy PID control system is as follows.

Fuzzy Logic

Defuzzifica tion

PID

Spe e d
me a sure me nt

Moto r
Drive r

BLDCM

Ha ll Se nso r

Fuzzy Logic
Contro lle r

Contro lle r

⨂
PWM출구)(tref

)(t





pk
ik dk

eck

Fig.6 Fuzzy PID speed control system of BLDCM

Simulation results

Based on Matlab/Simulink establish a model of BLDC control system, and the model simulate BLDCM speed
control system. The basic use of system simulation module in MATLAB/Simulink built in a fuzzy PID controller
and conventional PID simulation model, before simulation the first edited embedded fuzzy inference system
Fuzzy Logic Controller module.

Fig.7 Simulation frame diagram of control system using MATLAB/Simulink

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Table 2 Technical specifications of AGV

Specification Value

Load capacity Below 200kg

Navigation way Line following/Zigbee

Control method Button/Zigbee

Rated speed 0.6m/s

Maximum speed 1.2m/s

Walking Direction Forward, Backward

Minimum turning radius 1.3m

Noise level Below 40dB

Power supply 48V Battery

Drive wheel Two BLDC motors

Fig.14 AGV for transportation

BLDCM
Driver

Red: 5v +

Black: 5v Gnd

Green: Hall1

Blue: Hall2

Yellow: Hall3

Red: 5v +
Black: 5v Gnd
PWM

48V
+

48V
GND

key

48V-12V
Transformer

12V +

12V GND

Controll Unit

R S T

Lithium
Battery

48V GND

48V +

PWM output
Hall1

STM32duino_103C

Hall2 Hall3

Fig.15 Control circuit diagram of AGV

INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
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Page 2361

Fig.15 is control circuit diagram of AGV. 48V Lithium battery supply power to control board with Arduino
controller, motor drivers and other units such as relays and buttons after passing through 48V-12V transformer.
Hall sensor signals from motor is connected to driver and control board to measure current speed simultaneously.

CONCLUSION

This paper presents an improved speed control system of BLDCM for AGV. We measure current speed of the
robot using Hall-effect sensor of BLDCM and designed and implemented Fuzzy PID control system to improve
speed response and robustness of the speed control system of AGV. Simulations were made with two controllers:
conventional PID and FPID. Results show that proposed method satisfy control requirement of the AGV.

In the future, we will focus on improving accuracy and reliability of speed control system of BLDCM which are
widely used in application fields.

ACKNOWLEDGMENTS

I want to extend my hearty thanks to all the people who developed AGV with me and made a contribution to the
completion of my article.

Conflict of interests

The author declares no conflict of interest.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data Availability

The data that support the findings of this study are available within the article.

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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue X October 2025

Page 2362

10. K. Sarojini Devi, R. Dhanasekaran, S.Muthulakshmi, “Improvement of Speed Control Performance in

BLDC Motor Using Fuzzy PID Controller”, 2016 International Conference on Advanced
Communication Control and Computing Technologies (ICACCCT), 380-384

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