Tuning PID controller parameters of the DC motor with PSO algorithm

Şahin Yıldırım; Mehmet Safa Bingol; Sertac Savas

doi:10.1556/1848.2023.00698

Jump to Content

International Review of Applied Sciences and Engineering

Volume/Issue:: Volume 15: Issue 3

Tuning PID controller parameters of the DC motor with PSO algorithm

Authors:

Şahin Yıldırım

Şahin Yıldırım Mechatronics Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey

Search for other papers by Şahin Yıldırım in
Current site
Google Scholar
PubMed

sahiny@erciyes.edu.tr https://orcid.org/0000-0002-7149-3274

Mehmet Safa Bingol

Mehmet Safa Bingol Mechatronics Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey
Mechatronics Engineering, Engineering Faculty, Nigde Omer Halisdemir University, Nigde, Turkey

Search for other papers by Mehmet Safa Bingol in
Current site
Google Scholar
PubMed

, and

Sertac Savas

Sertac Savas Mechatronics Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey

Search for other papers by Sertac Savas in
Current site
Google Scholar
PubMed

View More View Less

Pages:: 281–286

Online Publication Date:: 17 Jan 2024

Publication Date:: 25 Oct 2024

Article Category:: Research Article

DOI:: https://doi.org/10.1556/1848.2023.00698

Keywords:: DC motor; particle swarm optimization; PID; filter coefficient

Open access

Download PDF

Abstract

Direct current (DC) motors have superior features such as operating at different speeds, being affordable and easily controllable. Therefore, DC motors have many uses, such as machine tools and robotic systems in many factories up to the textile industry. The PID controller is one of the most common methods used to control DC motors. PID is a feedback controller with the terms Proportional, Integral, and Derivative. The proper selection of P, I, and D parameters is critical for achieving the desired control in the PID controller. In this study, the transfer function of a DC motor is first obtained, and the speed of the DC motor is controlled by the PID controller using this transfer function. Then, Particle Swarm Optimization (PSO), an optimization method based on swarm intelligence, is used to adjust the P, I, and D parameters. By using the obtained P, I, and D coefficients, the speed of the DC motor is tried to be controlled, and the effect of the filter coefficient on the system output is examined. The performance of the proposed PSO-PID controller with successful results is given in tables and graphics. Control and optimization studies are carried out with MATLAB Simulink.

Abstract

1 Introduction

At the present time, direct current (DC) motors have a wide range of uses, from machine tools to robotic systems. In some unique and sensitive systems, it is critical that the motor provides the desired speed output. However, obtaining the desired speed output in open loop systems is difficult, especially due to external disturbances. As a result, a controller is needed for DC motor speed control. There are two types of control systems: open loop and closed loop. The Proportional Integral Derivative (PID), a closed-loop controller, is one of the approaches used to control systems. PID calculates the difference between the desired output and the actual output, called the error, and a control signal is generated according to this error value. It is essential to adjust the PID parameters to obtain the desired output. For this purpose, various optimization algorithms are used. One of them is particle swarm optimization (PSO) [1, 2].

PID is used in many applications such as control of bus suspension systems [3, 4], tracking problems of a mobile robot [5], upper limb rehabilitation robot [6], hybrid energy storage system for electric vehicles [7], trajectory tracking control of robot manipulator [8]. Baidya et al. used firefly-PID controller to reduce system error between the reference and output signals in electric vehicles [9]. The results obtained from the proposed controller are compared with classical PID control, firefly-PID controller, teaching learning-based optimization-PID controller, and PSO-PID controller. Ibrahim et al. used bacterial foraging and PSO technique to find the best PID controller parameters for controlling the speed of a brushless DC motor that is modeled in Simulink [10]. Lins and Krishnakumar presented tuning of PID controller for a PV-fed BLDC motor using PSO and teaching-learning-based optimization (TLBO) algorithm in their paper [11]. Gokce et al. analyzed PID control of brushed DC motor in their paper [12]. PID controller parameters are tuned with PSO and Ziegler-Nichols (ZN). Results show that PSO approach has clearly higher performance. In this study, firstly, a DC motor transfer function is obtained. Afterwards, this transfer function is controlled with PID, and PID parameters are optimized with PSO algorithm. The effect on the output has been examined by changing the N (filter coefficient) value.

In addition, the optimization performance of the PSO algorithm has been demonstrated in articles in which experimental studies have been carried out. Vishal et al. studied the determination of control parameters of an experimental DC motor using different optimization algorithms by minimizing Integral Time Absolute Error (ITAE) [13]. Payakkawan et al. used AI-based heuristic optimization techniques based on particle swarm optimization (PSO) in their studies. On-line tuning has also been performed on a real DC motor [14]. Rahayu et al. used PSO to optimize the rise time value, settling time and overshoot values in the speed control of an Arduino-controlled DC motor [15]. Palma et al. proposed an on-line optimization approach based on particle swarm optimization (PSO) for an experimental DC motor speed control. In this work both PID controllers and sliding mode controllers (SMC) are considered [16].

2 Materials and method

2.1 DC motor

DC motors widely used today generate rotational motion directly. This movement is used by transmitting with elements such as gears or belts. The electrical equivalent circuit of the armature is given in Fig. 1. This section [17] explains how to extract the DC motor transfer function used in this study.

Fig. 1.

The electrical equivalent circuit of the armature

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

T refers to motor torque; i refers to armature current. T is obtained by the product of K_t and i. The armature voltage e is proportional to the rotational speed and is shown by Equation (2).

T = K_{t} . i

(1)

e = K_{e} . \dot{θ}

(2)

Using Kirchoff's Law and Newton's Law, we get Equation (3) and Equation (4).

J \ddot{θ} + b \dot{θ} = K i

(3)

L \frac{d i}{d t} + R i = V - K \dot{θ}

(4)

Laplace transforms of Equation (3) and Equation (4) are made to obtain the transfer function of the DC motor.

s (J s + b) θ (s) = K I (s)

(5)

(L s + R) I (s) = V - K s θ (s)

(6)

As a result, the transfer function of the DC motor is found and given in Equation (7).

P (s) = \frac{\dot{θ} (s)}{V (s)} = \frac{K}{(J s + b) (L s + R) K^{2}}

(7)

The DC motor parameters used in this study are given in Table 1 [18].

Table 1.

DC motor parameters

Parameter	Symbol	Value
Motor inertia	J	0.01 (kg m² s⁻²)
Motor inductance	L	0.5 (H)
Motor resistance	R	1 (ohm)
Viscous friction	b	0.1 (N m s)
Torque constant	K	0.01 (N m/Amp)

The DC motor parameters given in Table 1 are used while creating the simulink model. J, L, R, b, and K represent motor inertia, motor inductance, motor resistance, viscous friction, torque constant, respectively.

2.2 PID control

One of the automatic control techniques is PID control technique. The PID control technique uses three parameters, proportional, integral, and derivative, to reduce the error. The control signal u(t) is calculated as Equation (8) [19]. While the steady-state error that may occur in the system is eliminated in the integral effect, the response speed and stability of the system are increased in the derivative effect. Thus, the PID control method makes the steady-state error zero in the system and ensures a fast and stable system response [20, 21]. Block diagram of the PID is given in Fig. 2.

u (t) = K p . e (t) + K i \int_{0}^{t} e (t) . d (t) + K d . \frac{d}{d t} . e (t)

(8)

Fig. 2.

Block diagram representation of the PID control scheme

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

When designing PID control systems, the main purpose is to set PID parameters and apply them to the system so that the desired output can be obtained.

2.3 Particle swarm optimization

The widespread use of PID controllers in the industry has increased the interest in tuning their parameters. Particle swarm optimization algorithm is proposed by Kennedy and Eberhart in 1995 [22]. PSO is developed by drawing on the foraging behavior of bird flocks. PSO is a calculation method that progresses the solution recursively, starting from a candidate solution to reach the best solution while optimizing the problem [23].

In PSO, particles represent the possible solutions, and the particle community refers to the flock. The particle symbolizes any bird in the search space. While birds in nature act based on their own experiences while searching for food, they also show the behavior of following the bird closest to food. Thus, the value of each particle reaches a better point with the effect of the flock memory [24]. Flowchart representation of the PSO algorithm is given in Fig. 3. A long distance of movement works in the first iterations, while this distance should be shortened in the last iterations. Thus, it will be easier to find the optimum value. Therefore, different PSO methods have come to the fore recently [25–27].

Fig. 3.

Flowchart representation of the PSO algorithm

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

In PSO algorithm, the initial positions of the particles are randomly generated in the relevant search space. Generally, the initial velocities of the particles are taken as zero. The fitness values of randomly generated positions are calculated using the objective function of the problem. According to the fitness values, the best position of each particle so far (xbest,i) and the position of the best of the population (gbest) are determined. The velocities of the particles are updated using these positions [28].

3 Simulation and results

In this study, the DC motor, whose transfer function is obtained, is tried to be controlled with a speed of 50 rad s⁻¹. First of all, the DC motor is operated with open loop. The graphic obtained is given in Fig. 4. As can be seen from the graph, the DC motor cannot provide the speed output as desired. The DC motor is controlled with PID control to achieve the desired output. The effect of PID parameters on the max overshoot, rise time, settling time, and steady-state error is enormous. Therefore, the PID parameters are optimized by the PSO method. For this optimization process, 50 particles (n = 50) are first used, then the same optimization process is carried out for 100 particles (n = 100). The PSO algorithm is run at 1000 iterations for both n values. The system is controlled using the PID parameters obtained for both. PID parameters obtained as a result of PSO are given in Table 2.

Fig. 4.

Velocity of DC motor for open loop

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

Table 2.

Optimized PID parameters with PSO

Number of particles	P	I	D
PSO-PID1 (n = 50)	167.3316	249.9998	12.8416
PSO-PID2 (n = 100)	249.7886	249.9960	18.0796

The MATLAB Simulink model used in this study is given in Fig. 5.

Fig. 5.

Simulink model of PID control

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

The model in Fig. 5 is run using the PSO parameters in Table 2. In addition, different N (filter coefficient) values are used. Three different values are used for the N value as 1, 10, and 100. These N values have been tested for both PSO-PID1 and PSO-PID2. The results obtained for max overshoot, rise time, settling time, and steady-state error are given in Table 3. The graphic obtained for PSO-PID1 is given in Fig. 6, and the graphic obtained for PSO-PID2 is given in Fig. 7. In Fig. 8, all results are given in a single graphic.

Table 3.

DC motor parameters

	N (filter coefficient)	Max overshoot (%)	Rise time (s)	Settling time (s)	Steady-state error (rad s⁻¹)
Open loop	–	–	–	–	45.01
PSO-PID1	1	39.98	0.0935	0.3975	0
	10	37.98	0.0756	0.3001	0
	100	0	0.0668	0.0668	0
PSO-PID2	1	43.58	0.0744	0.3316	0
	10	37.66	0.0600	0.2585	0
	100	1.86	0.0450	0.0450	0

Fig. 6.

PSO-PID1 control of DC motor velocity

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

Fig. 7.

PSO-PID2 control of DC motor velocity

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

Fig. 8.

PSO-PID1 and PSO-PID2 control of DC motor velocity

Citation: International Review of Applied Sciences and Engineering 15, 3; 10.1556/1848.2023.00698

As can be seen in Fig. 6, the maximum overshoot gets its best value when N is at its lowest value. For N = 1, the maximum overshoot is 39.98%, while for N = 10, the maximum overshoot decreased to 37.98%. When N = 100, no maximum overshoot is observed. The rise time is 0.0935 for N = 1 and 0.0668 for N = 100. Similarly, settling time decreased from 0.3975 to 0.0668. No steady state error occurred for any N value.

As can be seen in Fig. 7, the maximum overshoot got its best value when N was at its lowest value. While the maximum overshoot is 43.58% for N = 1, the maximum overshoot decreased to 1.86% for N = 100. The rise time is 0.0744 for N = 1 and 0.045 for N = 100. Similarly, settling time decreased from 0.3316 to 0.045. No steady state error occurred for any N value.

4 Conclusions

This paper has presented a proposed controller approach for DC motor velocity control. The PSO-PID controller results have better performance than the standard PID controller. The increase in the number of n particles in the PSO algorithm has not significantly contributed to finding the optimum PID parameters while increasing the computational load. Increasing the N (filter coefficient) value at a specific rate is crucial for the DC motor output to give the desired output. Although the same PID parameters are used, increasing the N value has resulted in a serious decrease, especially in the max overshoot. Also, rise time and settling time have decreased. This proposed controller structure will be employed in real-time applications of such systems.

References

[1]↑
S. M. H. Mousakazemi and N. Ayoobian, “Robust tuned PID controller with PSO based on two-point kinetic model and adaptive disturbance rejection for a PWR-type reactor,” Prog. Nucl. Energy, vol. 111, pp. 183–194, 2019. https://doi.org/10.1016/j.pnucene.2018.11.003.
- Search Google Scholar
- Export Citation
[2]↑
J. Wang, B. Qiang, W. Du, Z. He, S. Dong, and B. Guan, “Control technology for overhead crane system based on particle swarm algorithm optimization PID control,” in AIP Conference Proceedings, vol. 2073, no. 1, p. 020095, AIP Publishing LLC, 2019. https://doi.org/10.1063/1.5090749.
- Search Google Scholar
- Export Citation
[3]↑
S. Gaur and S. Jain, “Vibration control of bus suspension system using PI and PID controller,” Int. J. Adv. Eng. Sci., vol. 3, no. 3, pp. 94–99, 2013.
- Search Google Scholar
- Export Citation
[4]↑
A. Karthikraja, G. Petchinathan, and S. Ramesh, “Stochastic algorithm for PID tuning of bus suspension system,” in 2009 International Conference on Control, Automation, Communication and Energy Conservation, IEEE, 2009, pp. 1–6.
- Search Google Scholar
- Export Citation
[5]↑
M. Gheisarnejad and M. H. Khooban, “An intelligent Non-integer PID controller-based deep reinforcement learning: implementation and experimental results,” IEEE Trans. Ind. Electron., vol. 68, no. 4, pp. 3609–3618, 2020. https://doi.org/10.1109/TIE.2020.2979561.
- Search Google Scholar
- Export Citation
[6]↑
M. K. Joyo, Y. Raza, S. F. Ahmed, M. M. Billah, K. Kadir, K. Naidu, and Z. Mohd Yusof, “Optimized proportional-integral-derivative controller for upper limb rehabilitation robot,” Electronics, vol. 8, no. 8, p. 826, 2019. https://doi.org/10.3390/electronics8080826.
- Search Google Scholar
- Export Citation
[7]↑
K. Ye and P. Li, “A new adaptive PSO-PID control strategy of hybrid energy storage system for electric vehicles,” Adv. Mech. Eng., vol. 12, no. 9, 2020, 1687814020958574. https://doi.org/10.1177/1687814020958574.
- Search Google Scholar
- Export Citation
[8]↑
F. Loucif, S. Kechida, and A. Sebbagh, “Whale optimizer algorithm to tune PID controller for the trajectory tracking control of robot manipulator,” J. Braz. Soc. Mech. Sci. Eng., vol. 42, no. 1, pp. 1–11, 2020. https://doi.org/10.1007/s40430-019-2074-3.
- Search Google Scholar
- Export Citation
[9]↑
D. Baidya, S. Dhopte, and M. Bhattacharjee, “Sensing system assisted novel PID controller for efficient speed control of DC motors in electric vehicles,” IEEE Sensors Lett., vol. 7, no. 9, pp. 1–4, 2023. https://doi.org/10.1109/LSENS.2023.3234400.
- Search Google Scholar
- Export Citation
[10]↑
H. E. A. Ibrahim, F. N. Hassan, and A. O. Shomer, “Optimal PID control of a brushless DC motor using PSO and BF techniques,” Ain Shams Eng. J., vol. 5, no. 2, pp. 391–398, 2014. https://doi.org/10.1016/j.asej.2013.09.013.
- Search Google Scholar
- Export Citation
[11]↑
A. W. Lins and R. Krishnakumar, “Tuning of PID controller for a PV-fed BLDC motor using PSO and TLBO algorithm,” Appl. Nanoscience, vol. 13, no. 4, pp. 2911–2934, 2023. https://doi.org/10.1007/s13204-021-02272-x.
- Search Google Scholar
- Export Citation
[12]↑
C. O. Gokce, V. Durusu, and R. Unal, “Disturbance rejection performance comparison of PSO and ZN methods for various disturbance frequencies,” Int. J. Comput. Exp. Sci. Eng., vol. 9, no. 1, pp. 17–19, 2023. https://doi.org/10.22399/ijcesen.1202255.
- Search Google Scholar
- Export Citation
[13]↑
V. Vishal,V. Kumar, K. P. S. Rana, and P. Mishra, “Comparative study of some optimization techniques applied to DC motor control,” in 2014 IEEE International Advance Computing Conference (IACC), Gurgaon, India, 2014, pp. 1342–1347. https://doi.org/10.1109/IAdCC.2014.6779522.
- Search Google Scholar
- Export Citation
[14]↑
P. Payakkawan, K. Klomkarn, and P. Sooraksa, “Dual-line PID controller based on PSO for speed control of DC motors,” in 9th International Symposium on Communications and Information Technology, Icheon, Korea (South), 2009, pp. 134–139. https://doi.org/10.1109/ISCIT.2009.5341272.
- Search Google Scholar
- Export Citation
[15]↑
E. Rahayu, A. Ma'arif, and A. Çakan, “Particle swarm optimization (PSO) tuning of PID control on DC motor,” Int. J. Robotics Control Syst., vol. 2, no. 2, pp. 435–447, 2022. https://doi.org/10.31763/ijrcs.v2i2.476.
- Search Google Scholar
- Export Citation
[16]↑
L. B. Palma, F. V. Coito, B. G. Ferreira, and P. S. Gil, “PSO based on-line optimization for DC motor speed control,” in 9th International Conference on Compatibility and Power Electronics (CPE), Costa da Caparica, Portugal, 2015, pp. 301–306. https://doi.org/10.1109/CPE.2015.7231090.
- Search Google Scholar
- Export Citation
[17]↑
DC Motor Speed, System Modeling, 2023. https://ctms.engin.umich.edu/CTMS/index.php?example=MotorSpeed&section=SystemModeling.
- Search Google Scholar
- Export Citation
[18]↑
Z. Bingül, Matlab ve Simulink'le modelleme/kontrol, I, Birsen Press, 2005.
- Search Google Scholar
- Export Citation
[19]↑
T. Ahmet, O. Çakmak, and M. Gökbulut, “Tachogenerator DC motor speed control with PID and fuzzy logic,” J. Eng. Technol., vol. 1, no. 1, pp. 24–31. https://doi.org/20.500.12402/2274.
- Search Google Scholar
- Export Citation
[20]↑
T. Koçal and A. Muğan, “PID control of humidity and temperature exchange in a ship,” Gemi ve Deniz Teknolojisi, vol. 209, pp. 44–54, 2017.
- Search Google Scholar
- Export Citation
[21]↑
K. Aziz, “Comparative study on the tuning of the PID flight controllers using swarm intelligence,” Int. J. Aviation Sci. Technol., vol. 1, no. 02, pp. 80–91. https://doi.org/10.23890/IJAST.vm01is02.0205.
- Search Google Scholar
- Export Citation
[22]↑
R. Eberhart and J. Kennedy, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948. https://doi.org/10.1109/ICNN.1995.488968.
- Search Google Scholar
- Export Citation
[23]↑
H. R. Yazgan, F. Yener, S. Soysal, and A. E. Gür, “Comparison performances of PSO and GA to tuning PID controller for the DC motor,” Sakarya Univ. J. Sci., vol. 23, no. 2, pp. 162–174, 2019. https://doi.org/10.16984/saufenbilder.376464.
- Search Google Scholar
- Export Citation
[24]↑
M. Özmen, Y. Delice, and E. K. Aydoğan, “Customer segmentation with PSO in telecommunication sector,” Bilişim Teknolojileri Dergisi, vol. 11, no. 2, pp. 163–173, 2018. https://doi.org/10.17671/gazibtd.368460.
- Search Google Scholar
- Export Citation
[25]↑
H. L. Minh, S. Khatir, R. V. Rao, M. Abdel Wahab, and T. Cuong-Le, “A variable velocity strategy particle swarm optimization algorithm (VVS-PSO) for damage assessment in structures,” Eng. Comput., vol. 39, no. 2, pp. 1055–1084, 2023. https://doi.org/10.1007/s00366-021-01451-2.
- Search Google Scholar
- Export Citation
[26]
Z. Xing, J. Zhu, Z. Zhang, Y. Qin, and L. Jia, “Energy consumption optimization of tramway operation based on improved PSO algorithm,” Energy, vol. 258, 2022, Art no. 124848. https://doi.org/10.1016/j.energy.2022.124848.
- Search Google Scholar
- Export Citation
[27]
B. Wang, Z. S. Wu, Z. Zhao, and H. G. Wang, “Retrieving evaporation duct heights from radar sea clutter using particle swarm optimization (PSO) algorithm,” Prog. Electromagnetics Res., vol. 9, pp. 79–91, 2009. https://doi.org/10.2528/PIERM09090403.
- Search Google Scholar
- Export Citation
[28]↑
A. Ayari and S. Bouamama, “A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization,” Robotics Biomimetics, vol. 4, pp. 1–15, 2017.
- Search Google Scholar
- Export Citation

ProCite

RefWorks

Reference Manager

BibTeX

Zotero

EndNote

Save
Cite
Email this content

Share Link

Copy this link, or click below to email it to a friend
Email this content
or copy the link directly:

https://akjournals.com/view/journals/1848/15/3/article-p281.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

International Review of Applied Sciences and Engineering

Print ISSN:: 2062-0810

Online ISSN:: 2063-4269

Editorial Board

Mohammad Nazir AHMAD, Institute of Visual Informatics, Universiti Kebangsaan Malaysia, Malaysia

Murat BAKIROV, Center for Materials and Lifetime Management Ltd., Moscow, Russia

Nicolae BALC, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Umberto BERARDI, Toronto Metropolitan University, Toronto, Canada

Ildikó BODNÁR, University of Debrecen, Debrecen, Hungary

Sándor BODZÁS, University of Debrecen, Debrecen, Hungary

Fatih Mehmet BOTSALI, Selçuk University, Konya, Turkey

Samuel BRUNNER, Empa Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, Switzerland

István BUDAI, University of Debrecen, Debrecen, Hungary

Constantin BUNGAU, University of Oradea, Oradea, Romania

Shanshan CAI, Huazhong University of Science and Technology, Wuhan, China

Michele De CARLI, University of Padua, Padua, Italy

Robert CERNY, Czech Technical University in Prague, Prague, Czech Republic

Erdem CUCE, Recep Tayyip Erdogan University, Rize, Turkey

György CSOMÓS, University of Debrecen, Debrecen, Hungary

Tamás CSOKNYAI, Budapest University of Technology and Economics, Budapest, Hungary

Anna FORMICA, IASI National Research Council, Rome, Italy

Alexandru GACSADI, University of Oradea, Oradea, Romania

Eugen Ioan GERGELY, University of Oradea, Oradea, Romania

Janez GRUM, University of Ljubljana, Ljubljana, Slovenia

Géza HUSI, University of Debrecen, Debrecen, Hungary

Ghaleb A. HUSSEINI, American University of Sharjah, Sharjah, United Arab Emirates

Nikolay IVANOV, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia

Antal JÁRAI, Eötvös Loránd University, Budapest, Hungary

Gudni JÓHANNESSON, The National Energy Authority of Iceland, Reykjavik, Iceland

László KAJTÁR, Budapest University of Technology and Economics, Budapest, Hungary

Ferenc KALMÁR, University of Debrecen, Debrecen, Hungary

Tünde KALMÁR, University of Debrecen, Debrecen, Hungary

Milos KALOUSEK, Brno University of Technology, Brno, Czech Republik

Jan KOCI, Czech Technical University in Prague, Prague, Czech Republic

Vaclav KOCI, Czech Technical University in Prague, Prague, Czech Republic

Imre KOCSIS, University of Debrecen, Debrecen, Hungary

Imre KOVÁCS, University of Debrecen, Debrecen, Hungary

Angela Daniela LA ROSA, Norwegian University of Science and Technology, Trondheim, Norway

Éva LOVRA, Univeqrsity of Debrecen, Debrecen, Hungary

Elena LUCCHI, Eurac Research, Institute for Renewable Energy, Bolzano, Italy

Tamás MANKOVITS, University of Debrecen, Debrecen, Hungary

Igor MEDVED, Slovak Technical University in Bratislava, Bratislava, Slovakia

Ligia MOGA, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Marco MOLINARI, Royal Institute of Technology, Stockholm, Sweden

Henrieta MORAVCIKOVA, Slovak Academy of Sciences, Bratislava, Slovakia

Phalguni MUKHOPHADYAYA, University of Victoria, Victoria, Canada

Balázs NAGY, Budapest University of Technology and Economics, Budapest, Hungary

Husam S. NAJM, Rutgers University, New Brunswick, USA

Jozsef NYERS, Subotica Tech College of Applied Sciences, Subotica, Serbia

Bjarne W. OLESEN, Technical University of Denmark, Lyngby, Denmark

Stefan ONIGA, North University of Baia Mare, Baia Mare, Romania

Joaquim Norberto PIRES, Universidade de Coimbra, Coimbra, Portugal

László POKORÁDI, Óbuda University, Budapest, Hungary

Roman RABENSEIFER, Slovak University of Technology in Bratislava, Bratislava, Slovak Republik

Mohammad H. A. SALAH, Hashemite University, Zarqua, Jordan

Dietrich SCHMIDT, Fraunhofer Institute for Wind Energy and Energy System Technology IWES, Kassel, Germany

Lorand SZABÓ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Csaba SZÁSZ, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

Ioan SZÁVA, Transylvania University of Brasov, Brasov, Romania

Péter SZEMES, University of Debrecen, Debrecen, Hungary

Edit SZŰCS, University of Debrecen, Debrecen, Hungary

Radu TARCA, University of Oradea, Oradea, Romania

Zsolt TIBA, University of Debrecen, Debrecen, Hungary

László TÓTH, University of Debrecen, Debrecen, Hungary

László TÖRÖK, University of Debrecen, Debrecen, Hungary

Anton TRNIK, Constantine the Philosopher University in Nitra, Nitra, Slovakia

Ibrahim UZMAY, Erciyes University, Kayseri, Turkey

Andrea VALLATI, Sapienza University, Rome, Italy

Tibor VESSELÉNYI, University of Oradea, Oradea, Romania

Nalinaksh S. VYAS, Indian Institute of Technology, Kanpur, India

Deborah WHITE, The University of Adelaide, Adelaide, Australia

International Review of Applied Sciences and Engineering
Address of the institute: Faculty of Engineering, University of Debrecen
H-4028 Debrecen, Ótemető u. 2-4. Hungary
Email: irase@eng.unideb.hu

Indexing and Abstracting Services:

DOAJ
ERIH PLUS
Google Scholar
ProQuest
SCOPUS
Ulrich's Periodicals Directory

2024
Scopus
CiteScore
CiteScore rank
SNIP
Scimago
SJR index	0.261
SJR Q rank	Q2

2023
Scimago
Scimago H-index	11
Scimago Journal Rank	0.249
Scimago Quartile Score	Architecture (Q2) Engineering (miscellaneous) (Q3) Environmental Engineering (Q3) Information Systems (Q4) Management Science and Operations Research (Q4) Materials Science (miscellaneous) (Q3)
Scopus
Scopus Cite Score	2.3
Scopus CIte Score Rank	Architecture (Q1) General Engineering (Q2) Materials Science (miscellaneous) (Q3) Environmental Engineering (Q3) Management Science and Operations Research (Q3) Information Systems (Q3)
Scopus SNIP	0.751

International Review of Applied Sciences and Engineering
Publication Model	Gold Open Access Online only
Submission Fee	none
Article Processing Charge	1100 EUR/article
Regional discounts on country of the funding agency	World Bank Lower-middle-income economies: 50% World Bank Low-income economies: 100%
Further Discounts	Limited number of full waivers available. Editorial Board / Advisory Board members: 50% Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information	Gold Open Access

International Review of Applied Sciences and Engineering
Language	English
Size	A4
Year of Foundation	2010
Volumes per Year	1
Issues per Year	3
Founder	Debreceni Egyetem
Founder's Address	H-4032 Debrecen, Hungary Egyetem tér 1
Publisher	Akadémiai Kiadó
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	2062-0810 (Print)
ISSN	2063-4269 (Online)