Controlling of payload swinging of an overhead crane

Marouane Hmoumen; Tamàs Szabó

doi:10.1556/606.2021.00474

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Pollack Periodica

Volume/Issue:: Volume 17: Issue 2

Controlling of payload swinging of an overhead crane

Authors:

Marouane Hmoumen

Marouane Hmoumen Robert Bosch Department of Mechatronics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, H-3515, Miskolc, Hungary

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hhmoumen.marouane@gmail.com https://orcid.org/0000-0002-0304-117X

and

Tamàs Szabó

Tamàs Szabó Robert Bosch Department of Mechatronics, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Egyetemváros, H-3515, Miskolc, Hungary

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Pages:: 54–59

Online Publication Date:: 13 Dec 2021

Publication Date:: 07 Jun 2022

Article Category:: Research Article

DOI:: https://doi.org/10.1556/606.2021.00474

Keywords:: overhead crane; delay; stability; observer; payload

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Abstract

A new two-level hierarchical approach to control the trolley position and payload swinging of an overhead crane is proposed. At the first level, a simple mathematical pendulum model is investigated considering the time delay due to the use of a vision system. In the second level, a chain model is developed, extending the previous pendulum model considering the vibration of the suspending chain. The relative displacement of the payload is measured with a vision sensor, and the rest of the state-space variables are determined by a collocated observer. The gain parameters related to the state variables of the chain vibration are determined by the use of a pole placement method. The proposed controller is verified by numerical simulation and experimentally on a laboratory test bench.

Abstract

1 Introduction

The control of a crane can be applied to adopt an optimal theory of open-loop control [1] to minimize the swing of the payload. A modified input shaping control design method is presented in [2] to reduce residual vibration at the end-point and to limit the sway angle of the payload during traveling in crane systems. Furthermore, Cutforth et al. [3] designed an adaptive input shaping based upon flexible mode frequency variations to handle the uncertainties of the parameters. The open-loop technics, besides being easy to implement, there is no obligation for extra sensors in order to get the payload displacement, which saves costs [4]. However, there is a significant drawback of this approach that it is very responsive towards external disturbances.

In engineering practice the Proportional-Integral-Derivative (PID) controller is widely applicable method in controlling tasks [5]. Feedback control is known to be less affected by parameter variations and disturbances. In [6], the Generalized Predictive Control (GPC) and Linear Quadratic Gaussian (LQG) were well applied and compared for controlling the pendulation of the payload for an offshore crane. An anti-swing crane control method is developed in [7]. The authors built a novel manifold and the corresponding analysis, ensuring that the system state variables would converge to the equilibrium point.

In order to acquire the most realistic model of the crane system, several researchers have included other parameters in their models like damping and elasticity of the structure [8]. It has been known that time delay may produce significant damping of the oscillations [9].

A delayed reference non-collocated control approach for container cranes was developed by Sano et al. In [10] a delay due to the vision sensor was considered but parameters P and I of these controllers were not given. The authors of this paper analysed nonlinear and linear models of an overhead crane [11]. The results of the simulations showed that the vibration of the suspending chain is significant, and it should be considered.

In this work a new two-level hierarchical approach is proposed. At the first level, a simple mathematical pendulum model is investigated considering the time delay due to the use of a vision system. D-subdivision method [12] is applied to determine the stability regions expressed by gain parameters for different time delays. In the second level, a chain model is developed, extending the previous pendulum model considering the vibration of the suspending chain. However, only the relative displacement of the payload is measured with a vision sensor, and the rest of the variables of the state-space are determined by a collocated observer. The gain parameters associated with the payload are used from the first level model, and the rest of the parameters related to the state variables of the chain are determined by a pole placement method. The effectiveness of the proposed controller is verified by a numerical simulation and experimentally on a laboratory test bench. In the proposed method, the vibration of the chain suspending payload is considered by an observer. The motion of the payload is measured by a vision system and its delay is taken into consideration by the observer. The main novelty of this paper is the implementation of a new two-level hierarchical controlling approach, which considers vibration of the suspending chain and delay due to a vision system. The non-measured state variables are determined by a collocated observer.

2 Mathematical pendulum model of the overhead crane

In this Section, the model of the first level of the proposed hierarchical controller is described using a simple mathematical pendulum.

The motion of the crane payload in Fig. 1 can be written as:

x_{M} = u + x_{r},

(1)

y_{M} = - L \cos θ,

(2)

where u is the prescribed position of the trolley L, θ are the length and the angle of the pendulum, respectively, x _M, x _r is the horizontal absolute position, and

x_{r} = L \sin θ

is the relative position of the payload

M

Fig. 1.

Overhead-crane model (L = 0.8 m, M = 0.419 kg)

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

The Lagrangian approach is used to derive the mathematical model, which resulted a nonlinear differential equation, then it is linearized by assumption of small angles, i.e.,

\sin θ \approx θ

;

\cos θ \approx 1

\ddot{θ} + \frac{g}{L} θ = - \frac{\ddot{u}}{L} .

(3)

Since the position of the payload is controlled by the motion u of trolley, the following approximation is substituted in Eq. (3):

θ = sin θ = \frac{x_{r}}{L} = \frac{x_{M} - u}{L} .

(4)

The resulting equation of motion is expressed by the position x of the payload

{\ddot{x}}_{M} + \frac{g}{L} x_{M} = \frac{g}{L} u .

(5)

The main objective is to determine the position u of the trolley in Eq. (5) as a function of time to decrease the oscillation of the pendulum considering the delay of the system.

In order to design anti-swing control of the payload, the state variables must be measured. The experimental setup contains a machine vision system that is mounted on the trolley, and it determines the relative displacement of the payload. The measured data is sent to a PLC via Arduino Nano and D/A converter with a sample rate. The position of the trolley is updated in each time step $Δ t$ from the initial to a target position $x_{T}$ . It means that the state feedback has got a delay τ, which must be considered.

The control signal u is defined by the state feedback:

u (t) = - k_{1} (x_{M} (t - τ) - x_{T}) - k_{2} {\dot{x}}_{r} (t - τ) + x_{T} .

(6)

Substituting Eq. (6) into Eq. (5) and taking into consideration Eq. (1), the equation of motion with delay is obtained:

{\ddot{x}}_{M} (t) + \frac{g}{L} x_{M} (t) + \frac{g}{L} k_{1} x_{M} (t - τ) + \frac{g}{L} k_{2} {\dot{x}}_{M} (t - τ) = \frac{g}{L} [k_{2} \dot{u} (t) + (k_{1} + 1) x_{T}] .

(7)

Characteristic of Eq. (7) is written in Laplacian domain:

s^{2} + \frac{g}{L} + \frac{g}{L} k_{1} e^{- s τ} + \frac{g}{L} k_{2} s e^{- s τ} = 0.

(8)

In order to use D-subdivision method, substitution of

s = (i ω)

into Eq. (8) is required. After straightforward manipulations, the gain parameters can be separated and expressed as functions of ω:

k_{1} = (\frac{L}{g} ω^{2} - 1) cos (ω τ), k_{2} = (\frac{L}{g} ω^{2} - 1) sin (ω τ) .

(9)

The gain parameters in Eq. (9) determine stability regions of the system for different time delays τ (0.1 s, 0.15 s, 0.2 s), as it is shown in Fig. 2. The higher the time delay, the smaller the stability region.

Fig. 2.

Stability regions for different delays

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

Eq. (5) can also be written in state-space form with state variables x ₁ = x and x ₂ =

\dot{x}

[\begin{matrix} {\dot{x}}_{1} \\ {\dot{x}}_{2} \end{matrix}] = [\begin{matrix} 0 & 1 \\ - \frac{g}{L} & 0 \end{matrix}] [\begin{matrix} x_{1} \\ x_{2} \end{matrix}] + [\begin{matrix} 0 \\ - \frac{g}{L} \end{matrix}],

(10)

where the gravity acceleration g = 9.81 m/s².

The gains of a non-delayed linear system can be determined, e.g., using the pole placement method with MATLAB or Scilab software. According to the control theory, if the poles of the transfer function are located on the left half-space of the complex domain the linear system is stable. Prescribing the poles as p _1,2 = −0.12 ± i2.5, it provides gain parameters k ₁ = −0.5, k ₂ = 0.02, which are within the stable region of Fig. 2 for different time delays. This way, the stability of the system is granted. Equation (10) can be solved with its discrete-time state-space equation with time step

Δ t

, which is proportional with the delay of feedback:

x [n + 1] = A_{D} x [n] + b_{D} u [n], y [n] = c^{T} x [n] .

(11)

u [n] = - [k_{1} (x_{M} [n - 1] - x_{T}) + k_{2} {\dot{x}}_{r} [n - 1]] + x_{T},

(12)

where

x [k] = [\begin{matrix} x_{1} [n] \\ x_{2} [n] \end{matrix}]

A_{D} = [\begin{matrix} 0.8651897 & 0.1431969 \\ - 1.7559517 & 0.8651897 \end{matrix}]

b_{D} = [\begin{matrix} 0.1348103 \\ 1.7559517 \end{matrix}]

are determined using Scilab software with

Δ t = 0.15

s and y[n] is the output of the system, ie., the displacement of the payload, matrix c ^T =[0 1 0 0] is the output matrix. It is noted that in Eq. (12), the state variables of the feedback are given in time step n-1 in order to consider the delay

τ = Δ t

In the right-hand side of Eq. (12) the variables are taken in time step n-1 instead of n due to time delay τ=0.15 s. The results obtained for the control u[n] and x _r[n] are shown in Figs 3 and 4. The displacement of the trolley is shown in Fig. 3 . Figure 4 displays the displacement of the payload relative to the trolley. The simulation shows that the proposed controller provides good active damping; the vibration of the payload is suppressed effectively within 5 seconds.

Fig. 3.

Position of the trolley

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

Fig. 4.

Relative motion of the payload

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

The payload will inevitably be dominant in an extended model, where the suspending chain will be considered in the next Section. Therefore, the gain parameters k ₁ = −0.5, k ₂ = 0.02 related to the state variables payload will be used in the extended model.

3 Formulation of chain model

In the extended model, the vibration of the payload and the suspending chain is considered with a linear model of a taut string. The mass of the chain is m _L = 0.22 kg. It is assumed that the displacement of the payload is negligible in the vertical direction compared to the horizontal one. The chain in Fig. 5 can be discretized with two-node linear line elements.

Fig. 5.

Overhead-crane model ( $L = 0.8 m$ , $M = 0.419 kg$ , $m_{2} = 0.22 kg$ )

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

The present model containing two two-node elements considering the kinematical boundary condition, which provides a 2 degree of freedom (DoF) equation of motion. The use of lumped matrix instead of the consistent one, the motion of the equation in a simpler form can be given as

M \ddot{q} + K q = b_{1} u,

(13)

where

M = [\begin{matrix} μ L^{e} & 0 \\ 0 & \frac{μ L^{e}}{2} + M \end{matrix}]

K = [\begin{matrix} \frac{F^{1} + F^{2}}{L^{e}} & - \frac{F^{1}}{L^{e}} \\ - \frac{F^{2}}{L^{e}} & \frac{F^{2}}{L^{e}} \end{matrix}]

b_{1} = [\begin{matrix} - \frac{F^{1}}{L^{e}} \\ 0 \end{matrix}]

q = [\begin{matrix} x_{2} \\ x_{M} \end{matrix}]

In control theory, the state space form of the differential Eq. (13) is preferred, which is shown as:

\dot{x} = A x + b u,

(14)

where

A = [\begin{matrix} 0 & I \\ - M^{- 1} K & 0 \end{matrix}]

b = [\begin{matrix} 0 \\ M^{- 1} b_{1} \end{matrix}]

and

x = [\begin{matrix} q \\ \dot{q} \end{matrix}]

In this model the state vector contains four state variables. The objective of controlling the motion of the trolley is to damp the excessive swing of the payload within a short time. To achieve this, the following state feedback is used:

d ξ = d y

u = - [{\hat{k}}_{1} (x_{2} - x_{T}) + {\hat{k}}_{2} (x_{M} - x_{T}) + {\hat{k}}_{3} {\dot{x}}_{2} + {\hat{k}}_{4} {\dot{x}}_{M}] + x_{T},

(15)

where

\begin{matrix} {\hat{k}}_{1} & {\hat{k}}_{2} & {\hat{k}}_{3} & {\hat{k}}_{4} \end{matrix}

are gain parameters, which can be represented in a row vector

k_{D}^{T} = [\begin{matrix} {\hat{k}}_{1} & {\hat{k}}_{2} & {\hat{k}}_{3} & {\hat{k}}_{4} \end{matrix}]

The payload parameters ${\hat{k}}_{2}$ and ${\hat{k}}_{4}$ in (15) have the same role as k ₁ and k ₂ in Eq. (9) of the first level the hierarchical approach. Therefore, the values ${\hat{k}}_{2} = - 0.5$ , ${\hat{k}}_{4} = 0.02$ are also used in this level. Using pole placement function of Scilab 6.0.2 software and prescribing different stable poles and gain parameters, with the condition of keeping the previous values of ${\hat{k}}_{2}$ and ${\hat{k}}_{4}$ , the rest of the parameters ${\hat{k}}_{1}$ and ${\hat{k}}_{3}$ are associated to the chain vibration and they could be determined: ${\hat{k}}_{1} = - 0.07$ , ${\hat{k}}_{2} = - 0.5, {\hat{k}}_{3} = 0.003, {\hat{k}}_{4} = 0.02$ by these poles p ₁ = −0.0878902+2.3587906i, p ₂ = −0.0878902–2.3587906i, p ₃ = 0.2896223+16.901392i, p ₄ = −0.2896223–16.901392i.

4 Results of simulations and experiments

The parameters of the experimental crane model shown in Fig. 4 are given: mass of the chains is 0.22 kg, the payload is 0.419 kg, and the target distance of the trolley is x ₁ = 800 mm.

A simulation program has been developed under Scilab 6.0.2 software. The results of the simulation and the experiment in Figs 6 and 7 show a good agreement of the trolley motions. The simulation model correlates with the experimental results closely. However, results obtained for the relative motion of the payload display higher discrepancies in the beginning, but after four seconds, the results are converging. A minimal fluctuation is seen after four seconds can be explained by the digital circuit employed in the experimental setup. The overshooting for the experiment and the model for trolley motion is 87 and 98 mm, respectively. The relative motions of the payload converge to 0 between 5 and 6 seconds.

Fig. 6.

Comparing positions of the trolley

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

Fig. 7.

Comparing positions of the payload

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

The responses of the proposed controller have been tested for different gain parameters of the payload ${\hat{k}}_{2}, {\hat{k}}_{4}$ , with time step dt = 0.15 s. The previous gain parameters ${\hat{k}}_{1}, {\hat{k}}_{3}$ will be maintained.

The parameter ${\hat{k}}_{4} = 0.02$ is constant while different values of ${\hat{k}}_{2}$ were tested, as it is shown in Figs 8 and 9. The experimental results show that the trolley movement is not sensitive to the parameter change while the payload is a little bit more.

Fig. 8.

Positions of the trolley for different ${\hat{k}}_{2}$

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

Fig. 9.

Positions of the payload for different ${\hat{k}}_{2}$

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

In the following experiment, the parameter ${\hat{k}}_{2} = - 0.5$ is constant while different values of ${\hat{k}}_{4}$ were tested, as shown in Figs 10 and 11. The experimental results show that the motion of the system is more sensitive to the changes of this gain parameter compared to ${\hat{k}}_{2}$ .

Fig. 10.

Positions of the trolley for different ${\hat{k}}_{4}$

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

Fig. 11.

Positions of the payload for different ${\hat{k}}_{4}$

Citation: Pollack Periodica 17, 2; 10.1556/606.2021.00474

Finally different time increments $Δ t$ [0.10 s, 0.15 s, 0.20 s] have been tested. For the time increments $Δ t$ equal to 0.15 and 0.20 s the solutions convergent. However, for $Δ t$ = 0.10 s it gives undesirable oscillations at the vicinity of the target position.

The simulation results show that the displacement of the trolley and the swinging of the payload can be damped in approximately 4 to 6 seconds. The trolley quickly arrives at its target position within 2 seconds with no excessive overshooting. Then an additional 2 to 3 seconds are needed to fully damp the vibration of the payload.

5 Conclusion

This paper proposed a new control of trolley positioning and payload swinging of an overhead crane. A novel hierarchical approach is introduced, which has two levels. At the first level, the delay caused by the vision sensor is considered in a simple mathematical pendulum model by the use of the D-subdivision method, and the gain parameters of the feedback are determined.

At the second level, the vibration of the suspending chain is also considered in addition to the payload. Since the payload motion is dominant in the dynamics of the crane system; therefore, the gain parameters of the first level related to the payload's state variables are used in the extended model. The rest of the gain parameters related to the chain vibration are determined by a pole placement method. The displacement and velocity of the chain, i.e., the unmeasured state-variables, are determined by a collocated observer.

The effectiveness of the novel controller is verified by a test bench, which consists of a PLC controlled positioner of the trolley and a vision system providing the position feedback of the payload. The robustness of the designed controller was tested for different gain parameters and sampling times. The designed anti-swing controller with the tracking controller effectively reduces payload oscillations in a reasonable time, and its performance is comparable to the results of a theoretical model. The presented method is competitive with the existing methods.

Acknowledgements

The described article was carried out as part of the EFOP-3.6.1-16-2016-00011 “Younger and Renewing University - Innovative Knowledge City - institutional development of the University of Miskolc aiming at intelligent specialization” project implemented in the framework of the Szechenyi 2020 program. The realization of this project is supported by the European Union, co-financed by the European Social Fund.

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Pollack Periodica

Print ISSN:: 1788-1994

Online ISSN:: 1788-3911

Editorial Board

Bálint Bachmann (Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Jeno Balogh (Department of Civil Engineering Technology, Metropolitan State University of Denver, Denver, Colorado, USA)
Radu Bancila (Department of Geotechnical Engineering and Terrestrial Communications Ways, Faculty of Civil Engineering and Architecture, “Politehnica” University Timisoara, Romania)
Charalambos C. Baniotopolous (Department of Civil Engineering, Chair of Sustainable Energy Systems, Director of Resilience Centre, School of Engineering, University of Birmingham, U.K.)
Oszkar Biro (Graz University of Technology, Institute of Fundamentals and Theory in Electrical Engineering, Austria)
Ágnes Borsos (Institute of Architecture, Department of Interior, Applied and Creative Design, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Matteo Bruggi (Dipartimento di Ingegneria Civile e Ambientale, Politecnico di Milano, Italy)
Petra Bujňáková (Department of Structures and Bridges, Faculty of Civil Engineering, University of Žilina, Slovakia)
Anikó Borbála Csébfalvi (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Mirjana S. Devetaković (Faculty of Architecture, University of Belgrade, Serbia)
Szabolcs Fischer (Department of Transport Infrastructure and Water Resources Engineering, Faculty of Architerture, Civil Engineering and Transport Sciences Széchenyi István University, Győr, Hungary)
Radomir Folic (Department of Civil Engineering, Faculty of Technical Sciences, University of Novi Sad Serbia)
Jana Frankovská (Department of Geotechnics, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
János Gyergyák (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Kay Hameyer (Chair in Electromagnetic Energy Conversion, Institute of Electrical Machines, Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Germany)
Elena Helerea (Dept. of Electrical Engineering and Applied Physics, Faculty of Electrical Engineering and Computer Science, Transilvania University of Brasov, Romania)
Ákos Hutter (Department of Architecture and Urban Planning, Institute of Architecture, Faculty of Engineering and Information Technolgy, University of Pécs, Hungary)
Károly Jármai (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
Teuta Jashari-Kajtazi (Department of Architecture, Faculty of Civil Engineering and Architecture, University of Prishtina, Kosovo)
Róbert Kersner (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Rita Kiss (Biomechanical Cooperation Center, Faculty of Mechanical Engineering, Budapest University of Technology and Economics, Budapest, Hungary)
István Kistelegdi (Department of Building Structures and Energy Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Stanislav Kmeť (President of University Science Park TECHNICOM, Technical University of Kosice, Slovakia)
Imre Kocsis (Department of Basic Engineering Research, Faculty of Engineering, University of Debrecen, Hungary)
László T. Kóczy (Department of Information Sciences, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, University of Győr, Hungary)
Dražan Kozak (Faculty of Mechanical Engineering, Josip Juraj Strossmayer University of Osijek, Croatia)
György L. Kovács (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Balázs Géza Kövesdi (Department of Structural Engineering, Faculty of Civil Engineering, Budapest University of Engineering and Economics, Budapest, Hungary)
Tomáš Krejčí (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
Jaroslav Kruis (Department of Mechanics, Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic)
Miklós Kuczmann (Department of Automations, Faculty of Mechanical Engineering, Informatics and Electrical Engineering, Széchenyi István University, Győr, Hungary)
Tibor Kukai (Department of Engineering Studies, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Maria Jesus Lamela-Rey (Departamento de Construcción e Ingeniería de Fabricación, University of Oviedo, Spain)
János Lógó (Department of Structural Mechanics, Faculty of Civil Engineering, Budapest University of Technology and Economics, Hungary)
Carmen Mihaela Lungoci (Faculty of Electrical Engineering and Computer Science, Universitatea Transilvania Brasov, Romania)
Frédéric Magoulés (Department of Mathematics and Informatics for Complex Systems, Centrale Supélec, Université Paris Saclay, France)
Gabriella Medvegy (Department of Interior, Applied and Creative Design, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Tamás Molnár (Department of Visual Studies, Institute of Architecture, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Ferenc Orbán (Department of Mechanical Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Zoltán Orbán (Department of Civil Engineering, Institute of Smart Technology and Engineering, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Dmitrii Rachinskii (Department of Mathematical Sciences, The University of Texas at Dallas, Texas, USA)
Chro Radha (Chro Ali Hamaradha) (Sulaimani Polytechnic University, Technical College of Engineering, Department of City Planning, Kurdistan Region, Iraq)
Maurizio Repetto (Department of Energy “Galileo Ferraris”, Politecnico di Torino, Italy)
Zoltán Sári (Department of Technical Informatics, Institute of Information and Electrical Technology, Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Grzegorz Sierpiński (Department of Transport Systems and Traffic Engineering, Faculty of Transport, Silesian University of Technology, Katowice, Poland)
Zoltán Siménfalvi (Institute of Energy and Chemical Machinery, Faculty of Mechanical Engineering and Informatics, University of Miskolc, Hungary)
Andrej Šoltész (Department of Hydrology, Faculty of Civil Engineering, Slovak University of Technology in Bratislava, Slovakia)
Zsolt Szabó (Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Hungary)
Mykola Sysyn (Chair of Planning and Design of Railway Infrastructure, Institute of Railway Systems and Public Transport, Technical University of Dresden, Germany)
András Timár (Faculty of Engineering and Information Technology, University of Pécs, Hungary)
Barry H. V. Topping (Heriot-Watt University, UK, Faculty of Engineering and Information Technology, University of Pécs, Hungary)

POLLACK PERIODICA
Pollack Mihály Faculty of Engineering
Institute: University of Pécs
Address: Boszorkány utca 2. H–7624 Pécs, Hungary
Phone/Fax: (36 72) 503 650

E-mail: peter.ivanyi@mik.pte.hu

or amalia.ivanyi@mik.pte.hu

Indexing and Abstracting Services:

SCOPUS
CABELLS Journalytics

2023
Scopus
CiteScore	1.5
CiteScore rank	Q3 (Civil and Structural Engineering)
SNIP	0.849
Scimago
SJR index	0.288
SJR Q rank	Q3

Pollack Periodica
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