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

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https://orcid.org/0000-0002-7149-3274
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Mehmet Safa Bingol Mechatronics Engineering, Engineering Faculty, Erciyes University, Kayseri, Turkey
Mechatronics Engineering, Engineering Faculty, Nigde Omer Halisdemir University, Nigde, Turkey

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

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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

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.

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.
Fig. 1.

The electrical equivalent circuit of the armature

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00698

T refers to motor torque; i refers to armature current. T is obtained by the product of Kt and i. The armature voltage e is proportional to the rotational speed and is shown by Equation (2).
T=Kt.i
e=Ke.θ˙
Using Kirchoff's Law and Newton's Law, we get Equation (3) and Equation (4).
Jθ¨+bθ˙=Ki
Ldidt+Ri=VKθ˙
Laplace transforms of Equation (3) and Equation (4) are made to obtain the transfer function of the DC motor.
s(Js+b)θ(s)=KI(s)
(Ls+R)I(s)=VKsθ(s)
As a result, the transfer function of the DC motor is found and given in Equation (7).
P(s)=θ˙(s)V(s)=K(Js+b)(Ls+R)K2

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

Table 1.

DC motor parameters

ParameterSymbolValue
Motor inertiaJ0.01 (kg m2 s−2)
Motor inductanceL0.5 (H)
Motor resistanceR1 (ohm)
Viscous frictionb0.1 (N m s)
Torque constantK0.01 (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)=Kp.e(t)+Ki0te(t).d(t)+Kd.ddt.e(t)
Fig. 2.
Fig. 2.

Block diagram representation of the PID control scheme

Citation: International Review of Applied Sciences and Engineering 2024; 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.
Fig. 3.

Flowchart representation of the PSO algorithm

Citation: International Review of Applied Sciences and Engineering 2024; 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.
Fig. 4.

Velocity of DC motor for open loop

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00698

Table 2.

Optimized PID parameters with PSO

Number of particlesPID
PSO-PID1 (n = 50)167.3316249.999812.8416
PSO-PID2 (n = 100)249.7886249.996018.0796

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

Fig. 5.
Fig. 5.

Simulink model of PID control

Citation: International Review of Applied Sciences and Engineering 2024; 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 loop45.01
PSO-PID1139.980.09350.39750
1037.980.07560.30010
10000.06680.06680
PSO-PID2143.580.07440.33160
1037.660.06000.25850
1001.860.04500.04500
Fig. 6.
Fig. 6.

PSO-PID1 control of DC motor velocity

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00698

Fig. 7.
Fig. 7.

PSO-PID2 control of DC motor velocity

Citation: International Review of Applied Sciences and Engineering 2024; 10.1556/1848.2023.00698

Fig. 8.
Fig. 8.

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

Citation: International Review of Applied Sciences and Engineering 2024; 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.

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    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. 183194, 2019. https://doi.org/10.1016/j.pnucene.2018.11.003.

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    • Search Google Scholar
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    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. 9499, 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. 16.

    • Search Google Scholar
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  • [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. 36093618, 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. 111, 2020. https://doi.org/10.1007/s40430-019-2074-3.

    • Search Google Scholar
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  • [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. 14, 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. 391398, 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. 29112934, 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. 1719, 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. 13421347. 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. 134139. 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. 435447, 2022. https://doi.org/10.31763/ijrcs.v2i2.476.

    • Search Google Scholar
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Senior editors

Editor-in-Chief: Ákos, Lakatos University of Debrecen (Hungary)

Founder, former Editor-in-Chief (2011-2020): Ferenc Kalmár University of Debrecen (Hungary)

Founding Editor: György Csomós University of Debrecen (Hungary)

Associate Editor: Derek Clements Croome University of Reading (UK)

Associate Editor: Dezső Beke University of Debrecen (Hungary)

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 Ryerson 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

    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 Czech Republic

    György CSOMÓS University of Debrecen Debrecen Hungary

    Tamás CSOKNYAI Budapest University of Technology and Economics Budapest Hungary

    Eugen Ioan GERGELY University of Oradea Oradea Romania

    József FINTA University of Pécs Pécs Hungary

    Anna FORMICA IASI National Research Council Rome

    Alexandru GACSADI University of Oradea Oradea Romania

    Eric A. GRULKE University of Kentucky Lexington United States

    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

    Imra KOCSIS University of Debrecen Debrecen Hungary

    Imre KOVÁCS University of Debrecen Debrecen Hungary

    Angela Daniela LA ROSA Norwegian University of Science and Technology

    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 United States

    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

    Antal PUHL (1950–2023) University of Debrecen Debrecen 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

    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

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2022  
Scimago  
Scimago
H-index
9
Scimago
Journal Rank
0.235
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
1.6
Scopus
CIte Score Rank
Architecture 46/170 (73rd PCTL)
General Engineering 174/302 (42nd PCTL)
Materials Science (miscellaneous) 93/150 (38th PCTL)
Environmental Engineering 123/184 (33rd PCTL)
Management Science and Operations Research 142/198 (28th PCTL)
Information Systems 281/379 (25th PCTL)
 
Scopus
SNIP
0.686

2021  
Scimago  
Scimago
H-index
7
Scimago
Journal Rank
0,199
Scimago Quartile Score Engineering (miscellaneous) (Q3)
Environmental Engineering (Q4)
Information Systems (Q4)
Management Science and Operations Research (Q4)
Materials Science (miscellaneous) (Q4)
Scopus  
Scopus
Cite Score
1,2
Scopus
CIte Score Rank
Architecture 48/149 (Q2)
General Engineering 186/300 (Q3)
Materials Science (miscellaneous) 79/124 (Q3)
Environmental Engineering 118/173 (Q3)
Management Science and Operations Research 141/184 (Q4)
Information Systems 274/353 (Q4)
Scopus
SNIP
0,457

2020  
Scimago
H-index
5
Scimago
Journal Rank
0,165
Scimago
Quartile Score
Engineering (miscellaneous) Q3
Environmental Engineering Q4
Information Systems Q4
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q4
Scopus
Cite Score
102/116=0,9
Scopus
Cite Score Rank
General Engineering 205/297 (Q3)
Environmental Engineering 107/146 (Q3)
Information Systems 269/329 (Q4)
Management Science and Operations Research 139/166 (Q4)
Materials Science (miscellaneous) 64/98 (Q3)
Scopus
SNIP
0,26
Scopus
Cites
57
Scopus
Documents
36
Days from submission to acceptance 84
Days from acceptance to publication 348
Acceptance
Rate

23%

 

2019  
Scimago
H-index
4
Scimago
Journal Rank
0,229
Scimago
Quartile Score
Engineering (miscellaneous) Q2
Environmental Engineering Q3
Information Systems Q3
Management Science and Operations Research Q4
Materials Science (miscellaneous) Q3
Scopus
Cite Score
46/81=0,6
Scopus
Cite Score Rank
General Engineering 227/299 (Q4)
Environmental Engineering 107/132 (Q4)
Information Systems 259/300 (Q4)
Management Science and Operations Research 136/161 (Q4)
Materials Science (miscellaneous) 60/86 (Q3)
Scopus
SNIP
0,866
Scopus
Cites
35
Scopus
Documents
47
Acceptance
Rate
21%

 

International Review of Applied Sciences and Engineering
Publication Model Gold Open Access
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 waiver 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)

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