Authors:
Andrea Wéber Doctoral School of Multidisciplinary Engineering Sciences, Széchenyi István University, Győr, Hungary

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Miklós Kuczmann Department of Power Electronics and Electric Drives, Faculty of Power Electronics and Electric Drives, Széchenyi István University, Győr, Hungary

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Abstract

Present paper shows the different types of tensor product model based linear matrix inequality controller design and feasibility analysis of two degrees of freedom aeroelastic wing section model. The tensor product models are based on reducing or removing the nonlinear behavior of the system and weighting functions. The linear matrix inequality based method results globally asymptotically stable system. The goal of the paper is to examine that selecting and varying the transformation space influences the feasibility of the linear matrix inequality based controller. The paper gives a comparison between the different tensor product models in terms of controller performance. The linear matrix inequality gives feasible solution for the controller design if the transformation space is selected adequately.

Abstract

Present paper shows the different types of tensor product model based linear matrix inequality controller design and feasibility analysis of two degrees of freedom aeroelastic wing section model. The tensor product models are based on reducing or removing the nonlinear behavior of the system and weighting functions. The linear matrix inequality based method results globally asymptotically stable system. The goal of the paper is to examine that selecting and varying the transformation space influences the feasibility of the linear matrix inequality based controller. The paper gives a comparison between the different tensor product models in terms of controller performance. The linear matrix inequality gives feasible solution for the controller design if the transformation space is selected adequately.

1 Notations and definitions

Notations and definitions that used in present paper are

  • Indices: i=1,2,,I and j=1,2,,J, where I,J are the number of the Linear Time-Invariant system (LTI) vertexes;

  • System matrix S(p(t)) is determined with p=p(t)Ω;

  • Transformation space Ω=[a1,b1]×[a2,b2]×...×[an,bn]Rn, where the Tensor Product (TP) model representation is explainable;

  • Weighting functions wi=wi(p(t)), wj=wj(p(t));

  • Close to NOrmalised (CNO) type weighting function;

  • x(t)Rn is the state vector, u(t)Rm is the input vector, y(t)Rq is the output vector and Ai(t)Rn×n, Bi(t)Rn×m;

  • Matrices for Linear Matrix Inequality (LMI), Fi1,i2 is the controller gain, and Fi=MiP11, Fj=MjP11, where P1>0. Matrices P and M can be found by convex optimization methods including LMIs;

  • X-type weighting functions: Varying the input space with reduction of non-linearity results less complex weighting functions, (see in Fig. 1) where wn(pn(t)) are illustrated for n dimensions.

Fig. 1.
Fig. 1.

X-type weighting functions

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

2 Introduction

Present article shows the different types of TP models of the aeroelastic wing section with LMI based controller design in Parallel Distributed Compensation (PDC) framework via Higher Order Singular Value Decomposition (HOSVD). The proposed design method results stable controller and the paper proves that it can be selected the better TP model with better control performance. In order to achieve this, it is possible to vary the transformation space and select the parameter space.

Previous paper [1] describes the TP model transformation-based observer and controller design for the same Nonlinear Aeroelastic Test Apparatus (NATA) model. This paper presents that selecting the input space and transformation space has influence on the control performances. In the current paper, the parameters of the wing model, the state-space variables, and the quasi-Linear Parameter Varying (qLPV) state-space representation, the applied parameters and mathematical expressions are the same as in papers [1–3]. The TP transformation is executed on the “original” qLPV model, see in papers [2, 3]. Current paper presents several alternative TP models and shows the LMI feasibility analysis for these models.

Paper [4] proposes the nonlinearity reducing method and changing the number of the inputs for the TP model. The article describes a new type of method to the TP model transformation: how to vary the number of inputs of a given TP model. The paper focuses on how to manipulate the number of inputs of a given Takagi-Sugeno (TS) fuzzy model but also discusses the difference between TS fuzzy and TP models.

The TP model transformation and its extension, LMI and HOSVD based methods are a popular research field today, there are many publications about the investigation; [4–13]. Articles on practical applications of the TP model transformation: [14–24]. Additional papers used for current article: [25, 26].

Current paper presents that varying input space and transformation space has influence on the feasibility regions of LMI. In this paper, the weighting functions are all CNO types, the same grid density is applied for each TP models. Furthermore, if the nonlinearity is reduced, then the feasibility regions of LMI are increased. With these investigations, it can be determined which TP model results better control performance.

3 Novel contribution of the paper

Present paper investigates that reducing non-linearity from the TP models and selecting transformation space influence the design of LMI based controller and the TP model, respectively.

  • Statement 1. Reducing or removing the nonlinear behavior from the TP models results different TP models and different control performance and the feasibility regions are increased. Selecting the parameter space has influence on the feasibility regions of LMI.

  • Statement 2. Varying and selecting the transformation space influence the feasibility of the LMI based controller and the TP model. Selection of transformation space shows how the controller changes in LMIs, then it can be chosen the better controller performance. The feasibility test shows that the more the non-linearity decreases, the larger the feasibility region becomes.

Proves. The proves are based on the 2DoF aeroelastic wing section model. The examinations follow these key points:

  1. Removing non-linearity from TP models: varying the input space;

  2. Applying HOSVD based method in numerical way;

  3. HOSVD and TP model transformation generates the LTI vertex systems;

  4. LMI feasibility analysis: changing the transformation space;

  5. LMI based controller design.

Versions of TP models have different numbers of inputs or combinations of inputs. The key structure for transforming TP models is based on the complexity reduction technique developed in [27], which presents a singular value-based method for reducing a given set of fuzzy rules. The method is able to change the number of inputs and transform the nonlinearity between the fuzzy rules and the input dimensions. These features significantly increase the modeling capability of the TP structure, allowing further complexity reduction and more robust control optimization.

In the present research, the reduction of nonlinearity is achieved by removing nonlinearities: reducing the order of the system and eliminating as much nonlinearity as possible from the system of equations, e.g., sin(x3), cos(x3), etc. Alternative TP models can be generated from the original TP model, with the advantage that alternative models can have a different number of inputs, or the inputs of the TP model can be specified as a function of the original inputs.

4 TP model variants of the wing section

The mechanical model (Fig. 2) is a two-dimensional typical airfoil in horizontal flow, whose motion is determined by two independent degrees of freedom; vertical displacement (dive) and pitch. The study of the dynamic behaviour of the aeroelastic wing is based primarily on divergence and rudder reversal. The mathematical modeling of the 2DoF aeroelastic wing section model and the parameters are presented in papers [2] and [3].

Fig. 2.
Fig. 2.

Two-dimensional airfoil plate

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

The state variables of the mechanical model are:
x(t)=[x1(t)x2(t)x3(t)x4(t)]=[h(t)α(t)h˙(t)α˙(t)],
where x1(t) is the plunge displacement and x2(t) is the pitch displacement. The qLPV state space representation of the model:
x˙(t)=S(p(t))[x(t)u(t)],
where
p(t)=[U(t)x2(t)],
where U(t) is the free stream velocity and the system matrix is as follow:
S(p(t))=[S1(p(t))S2(p(t))],
S1(p(t))=[0000k1k2U2(t)p(kα(x2(t)))k3k4U2(t)q(kα(x2(t)))],
S2(p(t))=[100010c1(U(t))c2(U(t))g3U2(t)c3(U(t))c4(U(t))g4U2(t)],
where the mathematical expressions and parameters are described in papers [2] and [3].

4.1 TP model 1

This model is the “original” TP model, where the parameters are not changed [2–4]. The weighting functions are CNO types for all TP models. After using HOSVD method on the system matrix (4), the following TP model transformation describes the nonlinear model:
x˙i=13j=12wi(U)wj(x2)(Ai,jx+Bi,ju),
where
u=(i=13j=12wi(x1)wj(x2)Fi,j)x.

Therefore the number of LTI vertex models are 3×2=6. Here, the parameters are p1(t)=U(t), p2(t)=x2(t) and the transformation space Ω=[14,25]×[0.3,0.3] with grid density 137. The weighting functions of TP model 1 are shown in Fig. 3. It can be seen that there are w1,1,w1,2,w1,3 weighting functions for p1, and w2,1,w2,2 weighting functions for p2. The next step is to remove or reduce the nonlinear behavior of the TP model 1.

Fig. 3.
Fig. 3.

Weighting functions of TP model 1

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

4.2 TP model 2

To reduce the nonlinearity, new parameter space is introduced: p1(t)=U(t) and p2(t)=kα(x2(t)). The transformation space is the same, as in case of the TP model 1. Here, the generated LTI vertex systems are 3×2=6:
x˙i=13j=12wi(U)wj(kα(x2))(Ai,jx+Bi,ju).

Figure 4 shows that w1,1, w1,2, w1,3 weighting functions of parameter p1(t) are the same as in case of TP model 1. x2(t) has influence on the system matrix. w2,1, w2,2 are X-type weighting functions for p2(t).

Fig. 4.
Fig. 4.

Weighting functions of TP model 2

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

The nonlinear behavior of the weighting functions are removed in the second dimension, therefore the dimension of x2(t) has changed. It can be noticed that the complexity of the second dimension is decreased compared to the first TP model.

4.3 TP model 3

Here, a new parameter is introduced: p2(t)=U2(t), so the parameter space now is p1(t)=U(t), p2(t)=U2(t), p3(t)=x2(t) and the transformation space Ω=[14,25]×[142,252]×[0.3,0.3]. After executing HOSVD, there are 2×2×2=8 LTI vertex systems. The structure of the TP model transformation is as follow:
x˙i=12j=12k=12wi(U)wj(U2)wk(x2)(Ai,j,kx+Bi,j,ku).

The weighting functions w1,1, w1,2 and w2,1, w2,2 are X-type for parameters p1(t) and p2(t). For p3(t), w3,1, w3,2 functions are illustrated in Fig. 5. In the first two dimensions, the weighting functions are less complex and there is a new dimension but the maximum number of the weighting functions are two. The number of the LTI systems are increased but reducing the nonlinear behavior is still possible.

Fig. 5.
Fig. 5.

Weighting functions of TP model 3

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

4.4 TP model 4

This TP model is the combination of TP model 2 and 3. The parameters are p1(t)=U(t), p2(t)=U2(t) and p3(t)=kα(x2(t)), the transformation space is Ω=[14,25]×[142,252]×[0.3,0.3]. Here, the number of the LTI system are 2×2×2=8:
x˙i=12j=12k=12wi(U)wj(U2)wk(kα(x2))(Ai,j,kx+Bi,j,ku).

The weighting functions w1,1, w1,2, w2,1, w2,2 and w3,1, w3,2 are all X-types. Therefore, it can be seen that the maximum number of the functions are two and this model is more simpler than the previous three TP models.

5 LMI feasibility analysis

Changing the transformation space Ω has influence on the LMI feasibility regions. Therefore the analysis of the feasibility test shows whether or not exist solution for LMIs. Consider the following solver for LMI feasibleness problems L(x)<R(x), where R is the feasibility radius. The solver is minimizes t subject to L(x)<R(x)+tI. Therefore, t should be negative for feasible solution.

The analysis only includes the change of the transformation space in one dimension, i.e., the transformation space is not changed in several dimensions at the same time.

In the figures, x-axis shows pmin, y-axis shows pmax parameters. In the illustrations, it is presented that the LMI can be solved in the given interval in different dimensions. Thus, the regions marked with black dots are the feasible regions and the regions marked with empty circles are the non-feasible regions.

5.1 LMI feasibility regions of TP model 1

Figure 6 illustrates the LMI feasibility regions of the TP model 1. In the first case the transformation space is changed in the first dimension for parameter p1(t) and fixed in the second dimension on interval [,]×[0.3,0.3]. The figures presents the regions of the LMI feasibility from U=[1,5] to U=[40,534]. The second case shows extending of the transformation space Ω=[14,25]×[,] for parameter p2(t).

Fig. 6.
Fig. 6.

LMI feasibility regions of TP model 1, a) first case, b) second case

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

5.2 LMI feasibility regions of TP model 2

The LMI feasibility regions of TP model 2 are the same as the previous model (Fig. 6) on the examined interval. It can be seen that the transformation space is changed in the first dimension related to p1(t) on interval [,]×[0.3,0.3]. In the second case, the second dimension is changed on interval [14,25]×[,] for parameter p2(t). The figures show that the feasibility regions are the same within the interval considered, but the nonlinearity was removed. Feasibility growth occurs outside the examined interval.

5.3 LMI feasibility regions of TP model 3

Here, changing the transformation space is illustrated in Fig. 7. It can be seen that the first dimension is modified for p1(t)=U(t) and the others are fixed on interval [,]×[142,252]×[0.3,0.3]. Then, the third dimension is changed and the first and second dimensions are fixed on interval [14,25]×[142,252]×[,] for p3(t). It can be noticed that the LMI feasibility region is increased for parameter p1(t) compared to previous TP models.

Fig. 7.
Fig. 7.

LMI feasibility regions of TP model 3, a) first case, b) second case

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

5.4 LMI feasibility regions of TP model 4

The LMI feasibility analysis shows that the LMI based controller is feasible and regions and given intervals are the same as TP model 3, see in Fig. 7, but feasibility regions can occur outside the examined interval. The first case illustrates when the transformation space is changed in the first interval and the second and third intervals are fixed on [,]×[142,252]×[0.3,0.3]. The second case shows that the third dimension is changed in transformation space [14,25]×[142,252]×[,] for parameter p3(t). Consequently, this TP model covers larger LMI feasibility regions.

In summary, as the nonlinear property is gradually reduced, the LMI feasibility region increases, which can be proved by varying and extending the transformation space Ω.

6 Stable controller design

The method of LMI allows the stability of the system to be achieved if LMI is feasible for different TP models. The polytopic model with controller is asymptotically stable if there exist such positive definite matrices X>0 and Mi satisfies the following conditions:
XAiTAiX+MiTBiT+BiMi0,
for all i,
XAiTAiXXAjTAjX+MjTBiT+BiMj+MiTBjT+BjMi0,

for all i<jI and p(t):wi(p(t))t))pwj(p(t))=0, where i=1,,I, j=i+1,,I and I is the number of the LTI systems, from the feedback gain solutions X and Mi: Fi=MiX1.

The initial conditions for all TP models are [h,α,h˙,α˙]T=[0.01,0.1,0,0]T. Figure 8 shows the x1 and x2 state variables i.e., plunge displacement h and pitch displacment α, the u control signal of the wing section model. These figures also show the different TP models with reduced non-linearity. Therefore, TP models give different results for the LMI based controller in terms of stability. The present TP models are investigated based on the objective to reduce the oscillation rate for each model and to reach the steady state sooner.

Fig. 8.
Fig. 8.

State variables and control signals for all TP models, a) state variable x1; b) state variable x2; c) control signal u

Citation: Pollack Periodica 2024; 10.1556/606.2024.00888

It can be seen in case of state variables x1, the steady state is reached in about 0.4 to 0.8 seconds. The oscillation rate is highest between 0 and 0.3 seconds. The purple colour indicates the state variable x1 of TP model 1, i.e. the “original system”. It can be noticed that the oscillations are larger for TP models 1 and 3. The figure shows that TP model 4 stabilizes faster and has smaller oscillation magnitude.

Furthermore, Fig. 8 shows that in case of state variables x2, the system stabilizes faster between about 0 and 0.1 seconds forTP models 1 and 3, but for TP models 2, 4, the function decays faster and the system reaches steady state faster because there is less oscillation. For the latter two models, stabilisation occurs between about 0.4 and 0.8 seconds. It is clear that TP model 3 performs better for state variable x2.

Finally, the control signal u showed in Fig. 8, that illustrates that TP model 2 has better control performance. In case of TP model 1 and 3, the control signals have lower controller performance because the system takes longer to reach steady state.

7 Conclusion

Current paper presents the TP transformation based controller design of 2DoF aeroelastic wing section through HOSVD method in PDC framework. The controller is designed for each TP models and the controllers are accomplished with LMI based method. The paper gives a comparison between the TP models with different non-linearity via choosing parameter space and varying transformation spaces. Consequently, it can be noted that selecting transformation space and input space results different control performance. The LMI feasibility test shows that the smaller the nonlinearity, the larger the feasibility region of the TP models. Then, we can choose the better TP model with better control performance. As a result, the TP model 4 is the simpler model from point of reducing non-linearity and the controller performance.

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  • [1]

    A. Wéber and M. Kuczmann, “TP transformation based observer and controller design of 2DoF aeroelastic wing section model,” in 1st International Conference on Internet of Digital Reality, Gyor, Hungary, June 23–24, 2022, pp. 000017000022.

    • Search Google Scholar
    • Export Citation
  • [2]

    P. Baranyi, “Output feedback control of two-dimensional aeroelastic system,” J. Guidance, Control Dyn., vol. 29, no. 3, pp. 762766, 2006.

    • Search Google Scholar
    • Export Citation
  • [3]

    P. Baranyi, “Tensor-product model-based control of two-dimensional aeroelastic system,” J. Guidance, Control Dyn., vol. 29, no. 2, pp. 391400, 2006.

    • Search Google Scholar
    • Export Citation
  • [4]

    P. Baranyi, “How to vary the input space of a TS fuzzy model: a TP model transformation based approach,” IEEE Trans. Fuzzy Syst., vol. 30, no. 2, pp. 345356, 2020.

    • Search Google Scholar
    • Export Citation
  • [5]

    P. Baranyi, Y. Yam, and P. Várlaki, Tensor Product Model Transformation in Polytopic Model Based Control. 1st ed., CRC Press, Taylor and Francis Group, 2018.

    • Search Google Scholar
    • Export Citation
  • [6]

    G. Bergqvist and E. Larsson, “The higher order singular value decomposition: Theory and an application,” IEEE Signal Process. Mag. , vol. 27 no. 3, pp. 151154, 2010.

    • Search Google Scholar
    • Export Citation
  • [7]

    P. Szeidl and P. Várlaki, “HOSVD based canonical form for polytopic models of dynamic systems,” J. Adv. Comput. Intelligence Intell. Inform., vol. 13, no. 1, pp. 5260, 2009.

    • Search Google Scholar
    • Export Citation
  • [8]

    K. Tanaka and H. O. Wang, “Fuzzy regulators and fuzzy observers: a linear matrix inequality approach,” in Proceedings of the 36th IEEE Conference on Decision and Control, San Diego, CA, USA, December 12-12, 1997, pp. 13151320.

    • Search Google Scholar
    • Export Citation
  • [9]

    D. Tikk, P. Baranyi, and R. Patton, “Approximation properties of TP model forms and its consequences to TPDC design framework,” Asian J. Control, vol. 9, no. 3, pp. 221231, 2007.

    • Search Google Scholar
    • Export Citation
  • [10]

    P. Korondi, “Tensor product model transformation-based sliding surface design,” Acta Polytech. Hungarica, vol. 3, no. 4, pp. 2335, 2006.

    • Search Google Scholar
    • Export Citation
  • [11]

    Y. Yam, “Fuzzy approximation via grid point sampling and singular value decomposition,” IEEE Trans. Syst. Man, Cybernetics, Part B, vol. 27, no. 6, pp. 933951, 1997.

    • Search Google Scholar
    • Export Citation
  • [12]

    X. Liu, X. Xin, Z. Li, and Z. Chen, “Near optimal control based on the tensor-product technique,” IEEE Trans. Circuits Syst. Express Briefs, vol. 64, no. 5, pp. 560564, 2017.

    • Search Google Scholar
    • Export Citation
  • [13]

    K. Tanaka and H. O. Wang, Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach. John Wiley and Sons, 2001.

  • [14]

    A. Szollosi and P. Baranyi, “Influence of the tensor product model representation of qLPV models on the feasibility of linear matrix inequality,” Asian J. Control , vol. 18, no. 4, pp. 13281342, 2016.

    • Search Google Scholar
    • Export Citation
  • [15]

    A. Wéber and M. Kuczmann, “TP transformation based controller and observer design of the inverted pendulum,” Przeglad Elektrotechniczny, vol. 98, no. 10, pp. 3439, 2022.

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    B. Takarics and P. Baranyi, “Tensor-product-model-based control of a three degrees- of-freedom aeroelastic model,” J. Guidance, Control Dyn., vol. 36, no. 5, pp. 15271533, 2013.

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    F. Hajdu, “Numerical examination of nonlinear oscillators,” Pollack Period., vol. 13, no. 3, pp. 95106, 2018.

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Senior editors

Editor(s)-in-Chief: Iványi, Amália

Editor(s)-in-Chief: Iványi, Péter

 

Scientific Secretary

Miklós M. Iványi

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

 

2022  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
14
Scimago
Journal Rank
0.298
Scimago Quartile Score

Civil and Structural Engineering (Q3)
Computer Science Applications (Q3)
Materials Science (miscellaneous) (Q3)
Modeling and Simulation (Q3)
Software (Q3)

Scopus  
Scopus
Cite Score
1.4
Scopus
CIte Score Rank
Civil and Structural Engineering 256/350 (27th PCTL)
Modeling and Simulation 244/316 (22nd PCTL)
General Materials Science 351/453 (22nd PCTL)
Computer Science Applications 616/792 (22nd PCTL)
Software 344/404 (14th PCTL)
Scopus
SNIP
0.861

2021  
Web of Science  
Total Cites
WoS
not indexed
Journal Impact Factor not indexed
Rank by Impact Factor

not indexed

Impact Factor
without
Journal Self Cites
not indexed
5 Year
Impact Factor
not indexed
Journal Citation Indicator not indexed
Rank by Journal Citation Indicator

not indexed

Scimago  
Scimago
H-index
12
Scimago
Journal Rank
0,26
Scimago Quartile Score Civil and Structural Engineering (Q3)
Materials Science (miscellaneous) (Q3)
Computer Science Applications (Q4)
Modeling and Simulation (Q4)
Software (Q4)
Scopus  
Scopus
Cite Score
1,5
Scopus
CIte Score Rank
Civil and Structural Engineering 232/326 (Q3)
Computer Science Applications 536/747 (Q3)
General Materials Science 329/455 (Q3)
Modeling and Simulation 228/303 (Q4)
Software 326/398 (Q4)
Scopus
SNIP
0,613

2020  
Scimago
H-index
11
Scimago
Journal Rank
0,257
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
340/243=1,4
Scopus
Cite Score Rank
Civil and Structural Engineering 219/318 (Q3)
Computer Science Applications 487/693 (Q3)
General Materials Science 316/455 (Q3)
Modeling and Simulation 217/290 (Q4)
Software 307/389 (Q4)
Scopus
SNIP
1,09
Scopus
Cites
321
Scopus
Documents
67
Days from submission to acceptance 136
Days from acceptance to publication 239
Acceptance
Rate
48%

 

2019  
Scimago
H-index
10
Scimago
Journal Rank
0,262
Scimago
Quartile Score
Civil and Structural Engineering Q3
Computer Science Applications Q3
Materials Science (miscellaneous) Q3
Modeling and Simulation Q3
Software Q3
Scopus
Cite Score
269/220=1,2
Scopus
Cite Score Rank
Civil and Structural Engineering 206/310 (Q3)
Computer Science Applications 445/636 (Q3)
General Materials Science 295/460 (Q3)
Modeling and Simulation 212/274 (Q4)
Software 304/373 (Q4)
Scopus
SNIP
0,933
Scopus
Cites
290
Scopus
Documents
68
Acceptance
Rate
67%

 

Pollack Periodica
Publication Model Hybrid
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Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription fee 2023 Online subsscription: 336 EUR / 411 USD
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Pollack Periodica
Language English
Size A4
Year of
Foundation
2006
Volumes
per Year
1
Issues
per Year
3
Founder Akadémiai Kiadó
Founder's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 1788-1994 (Print)
ISSN 1788-3911 (Online)

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