Nauki Techniczne

Archives of Control Sciences

Zawartość

Archives of Control Sciences | 2021 | vol. 31 | No 3 |

Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

The problem of control of rod heating process by changing the temperature along the rod whose ends are thermally insulated is considered. It is assumed that, along with the classical boundary conditions, nonseparated multipoint intermediate conditions are also given. Using the method of separation of variables and methods of the theory of control of finite-dimensional systems with multipoint intermediate conditions, a constructive approach is proposed to build the sought function of temperature control action. A necessary and sufficient condition is obtained, which the function of the distribution of the rod temperature must satisfy, so that under any feasible initial, nonseparated intermediate, and final conditions, the problem is completely controllable. As an application of the proposed approach, control action with given nonseparated conditions on the values of the rod temperature distribution function at the two intermediate moments of time is constructed.
Przejdź do artykułu

Bibliografia

[1] A.G. Butkovskii: Control Methods for Systems with Distributed Parameters. Nauka, 1975 (in Russian).
[2] A.G Butkovskii, S.A Malyi, and Yu.N. Andreev: Optimal Control of Metal Heating. Moscow, Metallurgy, 1972 (in Russian).
[3] A.I. Egorov: Optimal Control of Thermal and Diffusion Processes. Nauka, 1978 (in Russian).
[4] A.I. Egorov and L.N. Znamenskaya: Introduction to the Theory of Control of Systems with Distributed Parameters. Textbook, Saint Petersburg, LAN, 2017 (in Russian).
[5] E.Ya. Rapoport: Structural Modeling of Objects and Control Systems with Distributed Parameters. Higher School, 2003 (in Russian).
[6] A.N. Tikhonov and A.A. Samarskii: Equations of Mathematical Physics. Nauka, 1977 (in Russian).
[7] V.I. Ukhobotov and I.V. Izmest’ev: A control problem for a rod heating process with unknown temperature at the right end and unknown density of the heat source. Trudy Instituta Matematiki i Mekhaniki, UrO RAN, 25(1), (2019), 297–305 (in Russian), DOI: 10.21538/0134-4889-2019-25-1-297-305.
[8] V.I. Ukhobotov and I.V. Izmest’ev: The problem of controlling the process of heating the rod in the presence of disturbance and uncertainty. IFAC Papers OnLine, 51(32), (2018), 739–742, DOI: 10.1016/j.ifacol.2018.11.458.
[9] V.I. Butyrin and L.A. Fylshtynskyi: Optimal control of the temperature field in the rod when changing the control zone programmatically. Applied Mechanics, 12(84), (1976), 115–118 (in Russian).
[10] M.M. Kopets: Optimal control over the process of heating of a thin core. Reports of the National Academy of Sciences of Ukraine, 7, (2014), 48–52 (in Ukrainian), http://dspace.nbuv.gov.ua/handle/123456789/87951.
[11] N.V.Gybkina, D.Yu. Podusov, and M.V. Sidorov: The optimal control of a homogeneous rod final temperature state. Radioelectronics and Informatics, 2 (2014), 9–15 (in Russian).
[12] E.Y. Vedernikova and A.A. Kornev: To the problem of rod heating. Moscow Univ. Math. Bull., 69, (2014), 237–241, DOI: 10.3103/S0027132214060023.
[13] J.F. Bonnans and P. Jaisson: Optimal control of a parabolic equation with time-dependent state constraints. SIAM Journal on Control and Optimization, 48(7), (2010), 4550–4571.
[14] A. Lapin and E. Laitinen: Iterative solution of mesh constrained optimal control problems with two-level mesh approximations of parabolic state equation. Journal of Applied Mathematics and Physics, 6, (2018), 58–68, DOI: 10.4236/jamp.2018.61007.
[15] K. Kunisch and L. Wang: Time optimal control of the heat equation with pointwise control constraints. ESAIM: Control, Optimisation and Calculus of Variations, 19(2), (2013), 460–485, http://eudml.org/doc/272753.
[16] J.M. Lemos, L. Marreiro, and B. Costa: Supervised multiple model adaptive control of a heating fan. Archives of Control Sciences, 18(1), (2008), 5–16.
[17] S.H. Jilavyan, E.R. Grigoryan, and A.Zh. Khurshudyan: Heating control of a finite rod with a mobile source. Archives of Control Sciences, 31(2), (2021), 417–430, DOI: 10.24425/acs.2021.137425.
[18] V.R. Barseghyan: Control problem of string vibrations with inseparable multipoint conditions at intermediate points in time. Mechanics of Solids, 54(8), (2019), 1216–1226. DOI: 10.3103/S0025654419080120.
[19] V.R. Barseghyan: Optimal control of string vibrations with nonseparate state function conditions at given intermediate instants. Automation and Remote Control, 81(2), (2020), 226–235, DOI: 10.1134/S0005117920020034.
[20] V.R. Barseghyan: Control of Compound Dynamic Systems and of Systems with Multipoint Intermediate Conditions. Nauka, 2016 (in Russian).
[21] V.R. Barseghyan and T.V. Barseghyan: On an approach to the problems of control of dynamic system with nonseparated multipoint intermediate conditions. Automation and Remote Control, 76(4), (2015), 549–559, DOI: 10.1134/S0005117915040013.
Przejdź do artykułu

Autorzy i Afiliacje

Vanya R. Barseghyan
1

  1. Institute of Mechanics of the National Academyof Sciences of Armenia, Yerevan State University, Armenia
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

The solar photovoltaic output power fluctuates according to solar irradiation, temperature, and load impedance variations. Due to the operating point fluctuations, extracting maximum power from the PV generator, already having a low power conversion ratio, becomes very complicated. To reach a maximum power operating point, a maximum power point tracking technique (MPPT) should be used. Under partial shading condition, the nonlinear PV output power curve contains multiple maximum power points with only one global maximum power point (GMPP). Consequently, identifying this global maximum power point is a difficult task and one of the biggest challenges of partially shaded PV systems. The conventional MPPT techniques can easily be trapped in a local maximum instead of detecting the global one. The artificial neural network techniques used to track the GMPP have a major drawback of using huge amount of data covering all operating points of PV system, including different uniform and non-uniform irradiance cases, different temperatures and load impedances. The biological intelligence techniques used to track GMPP, such as grey wolf algorithm and cuckoo search algorithm (CSA), have two main drawbacks; to be trapped in a local MPP if they have not been well tuned and the precision-transient tracking time complex paradox. To deal with these drawbacks, a Distributive Cuckoo Search Algorithm (DCSA) is developed, in this paper, as GMPP tracking technique. Simulation results of the system for different partial shading patterns demonstrated the high precision and rapidity, besides the good reliability of the proposed DCSAGMPPT technique, compared to the conventional CSA-GMPPT.
Przejdź do artykułu

Bibliografia

[1] Zhao Zhuoli, Runting Cheng, Baiping Yan, Jiexiong Zhang, Ze- han Zhang, Mingyu Zhang, and Loi Lei Lai: A dynamic particles MPPT method for photovoltaic systems under partial shading conditions. Energy Conversion and Management, 220 (2020), 113070, DOI: 10.1016/j.enconman.2020.113070.
[2] Nabil A. Ahmed and Masafumi Miyatake: A novel maximum power point tracking for photovoltaic applications under partially shaded insolation conditions. Electric Power Systems Research, 78(5), (2008), 777–784, DOI: 10.1016/j.epsr.2007.05.026.
[3] Liqun Liu, Xiaoli Meng, and Chunxia Liu: A review of maximum power point tracking methods of PV power system at uniform and partial shading. Renewable and Sustainable Energy Reviews, 53 (2016), 1500–1507, DOI: 10.1016/j.rser.2015.09.065.
[4] Yanzhi Wang, Xue Lin, Younghyun Kim, Naehyuck Chang, and Mas- soud Pedram: Enhancing efficiency and robustness of a photovoltaic power system under partial shading. Thirteenth International Symposium on Quality Electronic Design (ISQED), Santa Clara USA, (2012), 592–600, DOI: 10.1109/ISQED.2012.6187554.
[5] Ricardo Orduz, Jorge Solorzano, Miguel Ángel Egido, and Ed- uardo Roman: Analytical study and evaluation results of power optimizers for distributed power conditioning in photovoltaic arrays. Progress in Photovoltaics: Research and Applications, 21(3), (2013), 359–373, DOI: 10.1002/pip.1188.
[6] Kashif Ishaque and Zainal Salam: A review of maximum power point tracking techniques of PV system for uniform insolation and partial shading condition. Renewable and Sustainable Energy Reviews, 19 (2013), 475–488, DOI: 10.1016/j.rser.2012.11.032.
[7] Jubaer Ahmed and Zainal Salam: A critical evaluation on maximum power point tracking methods for partial shading in PV systems. Renewable and Sustainable Energy Reviews, 47 (2015), 933–953, DOI: 10.1016/j.rser.2015.03.080.
[8] Ali M. Eltamaly: Performance of MPPT techniques of photovoltaic systems under normal and partial shading conditions. Advances in Renewable Energies and Power Technologies, vol. 1, Solar and Wind Energies, I. Yahyaoui, 2018, Elsevier, Chapter 4, 115–161.
[9] Ali M. Eltamaly: Performance of smart maximum power point tracker under partial shading conditions of photovoltaic systems. Journal ofRenewable and Sustainable Energy, 7(4), (2015), 043141, DOI: 10.1063/1.4929665.
[10] A. Talha, H. Boumaaraf, and O. Bouhali: Evaluation of maximum power point tracking methods for photovoltaic systems. Archives of Control Sciences, 21(2), (2011), 151–165.
[11] Hegazy Rezk and Ali M. Eltamaly: A comprehensive comparison of different MPPT techniques for photovoltaic systems. Solar Energy, 112 (2015), 1–11, DOI: 10.1016/j.solener.2014.11.010.
[12] S. Lyden and M.E. Haque: Maximum power point tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis. Renewable and Sustainable Energy Reviews, 52 (2015): 1504–1518, DOI: 10.1016/j.rser.2015.07.172.
[13] Zainal Salam, Jubaer Ahmed, and Benny S. Merugu: The application of soft computing methods for MPPT of PV system: A technological and status review. Applied Energy, 107 (2013), 135–148, DOI: 10.1016/j.apenergy.2013.02.008.
[14] Hassan M.H. Farh, Mohamed F. Othman, and Ali M. Eltamaly: Maximum power extraction from grid-connected PV system. Saudi Arabia Smart Grid (SASG), (2017), 1–6, DOI: 10.1109/SASG.2017.8356498.
[15] Seyedali Mirjalili, Seyed Mohammad Mirjalili, and Andrew Lewis: GreyWolf optimizer. Advances in Engineering Software, 69 (2014), 46–61, DOI: 10.1016/j.advengsoft.2013.12.007.
[16] Sabrina Titri, Cherif Larbes, Kamal Youcef Toumi, and Karima Be- natchba: A new MPPT controller based on the ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Applied Soft Computing, 58 (2017), 465–479, DOI: 10.1016/j.asoc.2017.05.017.
[17] Lian Lian Jiang, Douglas L. Maskell, and Jagdish C. Patra:Anovel ant colony optimization-based maximum power point tracking for photovoltaic systems under partially shaded conditions. Energy and Buildings, 58 (2013), 227–236, DOI: 10.1016/j.enbuild.2012.12.001.
[18] Lian Lian Jiang, R. Srivatsan, and Douglas L. Maskell: Computational intelligence techniques for maximum power point tracking in PV systems: A review. Renewable and Sustainable Energy Reviews, 85 (2018), 14–45, DOI: 10.1016/j.rser.2018.01.006.
[19] Ali M. Eltamaly and Hassan M.H. Farh: Dynamic global maximum power point tracking of the PV systems under variant partial shading using hybrid GWO-FLC. Solar Energy, 177 (2019), 306–316, DOI: 10.1016/j.solener.2018.11.028.
[20] Jubaer Ahmed and Zainal Salam: A maximum power point tracking (MPPT) for PV system using cuckoo search with partial shading capability. Applied Energy, 119 (2014), 118–130, DOI: 10.1016/j.apenergy.2013.12.062.
[21] Xin-She Yang and Suash Deb: Cuckoo search via Lévy flights. 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Coimbatore, India (2009), 210–214, DOI: 10.1109/NABIC.2009.5393690.
[22] Jubaer Ahmed and Zainal Salam: A soft computing MPPT for PV system based on cuckoo search algorithm. 4th International Conference on Power Engineering, Energy and Electrical Drives, Istanbul, Turkey, (2013), 558– 562, DOI: 10.1109/PowerEng.2013.6635669.
[23] Ahmed A. El Baset, A. El Halim, Naggar H. , and Ahmed A. El Sattar: A comparative study between perturb and observe and cuckoo search algorithm for maximum power point tracking. 21st International Middle East Power Systems Conference (MEPCON), Cairo, Egypt, (2019), 716–723, DOI: 10.1109/MEPCON47431.2019.9008210.
[24] Filippo Spertino and Jean Sumaili Akilimali: Are manufacturing I–V mismatch and reverse currents key factors in large photovoltaic arrays? IEEE Transactions on Industrial Electronics, 56(11), (2009), 4520–4531, DOI: 10.1109/TIE.2009.2025712.
[25] M. Drif, P.J. Perez, J. Aguilera, and J.D. Aguilar: A new estimation method of irradiance on a partially shaded PV generator in grid-connected photovoltaic systems. Renewable Energy, 33(9), (2008), 2048–2056, DOI: 10.1016/j.renene.2007.12.010.
[26] Bidyadhar Subudhi and Raseswari Pradhan: A comparative study on maximum power point tracking techniques for photovoltaic power systems. IEEE Transactions on Sustainable Energy, 4(1), (2012), 89–98, DOI: 10.1109/TSTE.2012.2202294.
[27] Kashif Ishaque and Zainal Salam:AcomprehensiveMATLAB Simulink PV system simulator with partial shading capability based on two-diode model. Solar Energy, 85(9), (2011), 2217–2227, DOI: 10.1016/j.solener.2011.06.008.
[28] Mohamed I.Mosaad, M. Osama Abed el-Raouf, Mahmoud A. Al- Ahmar, and Fahd A. Banakher: Maximum power point tracking of PV system based cuckoo search algorithm; review and comparison. Energy Procedia, 162 (2019), 117–126, DOI: 10.1016/j.egypro.2019.04.013.
[29] Bo Yang, JingboWang, Xiaoshun Zhang, Tao Yu, Wei Yao, Hongchun Shu, Fang Zeng, and Liming Sun: Comprehensive overview of metaheuristic algorithm applications on PV cell parameter identification. Energy Conversion and Management, 208 (2020), 112595, DOI: 10.1016/j.enconman.2020.112595.
[30] Tong Kang, Jiangang Yao, Min Jin, Shengjie Yang, and Thanh Long Duong: A novel improved cuckoo search algorithm for parameter estimation of photovoltaic (PV) models. Energies, 11(5), (2018), 1060, DOI: 10.3390/en11051060.
[31] S. Walton, O. Hassan, K. Morgan, and M.R. Brown: Modified cuckoo search: a new gradient free optimisation algorithm. Chaos, Solitons & Fractals, 44(9), (2011), 710718, DOI: 10.1016/j.chaos.2011.06.004.
[32] Amir Hossein Gandomi, Xin-She Yang, and Amir Hossein Alavi: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Engineering with Computers, 29(1), (2013), 17–35, DOI: 10.1007/s00366-011-0241-y.
[33] Abdesslem Layeb: A novel quantum inspired cuckoo search for knapsack problems. International Journal of Bio-Inspired Computation, 3(5), (2011), 297–305, DOI: 10.1504/IJBIC.2011.042260.
[34] Ehsan Valian, Saeed Tavakoli, Shahram Mohanna, and Atiyeh Haghi: Improved cuckoo search for reliability optimization problems. Computers & Industrial Engineering, 64(1), (2013), 459–468, DOI: 10.1016/j.cie.2012.07.011.
[35] Xiangtao Li, Jianan Wang, and Minghao Yin: Enhancing the performance of cuckoo search algorithm using orthogonal learning method. Neural Computing and Applications, 24(6), (2014), 1233–1247, DOI: 10.1007/s00521-013-1354-6.
[36] Hui Wang, Wenjun Wang, Hui Sun, Zhihua Cui, Shahryar Rahna- mayan, and Sanyou Zeng: A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Computing, 21(15), (2017), 4297–4307, DOI: 10.1007/s00500-016-2062-9.
[37] Wang Jianzhou, He Jiang, Yujie Wu, and Yao Dong: Forecasting solar radiation using an optimized hybrid model by cuckoo search algorithm. Energy, 81 (2015), 627–644, DOI: 10.1016/j.energy.2015.01.006.
[38] Wen Long, Shaohong Cai, Jianjun Jiao, Ming Xu, and Tiebin Wu: A new hybrid algorithm based on grey wolf optimizer and cuckoo search for parameter extraction of solar photovoltaic models. Energy Conversion and Management, 203 (2020), 112243, DOI: 10.1016/j.enconman.2019.112243.
[39] Diego Oliva, Ahmed A. Ewees, Mohamed Abd El Aziz, Aboul Ella Hassanien, and Marco Perez-Cisneros: A chaotic improved artificial bee colony for parameter estimation of photovoltaic cells. Energies, 10(7), (2017), 865, DOI: 10.3390/en10070865.
[40] Xiaofang Yuan, Yuqing He, and Liangjiang Liu: Parameter extraction of solar cell models using chaotic asexual reproduction optimization. Neural Computing and Applications, 26(5), (2015), 1227–1239, DOI: 10.1007/s00521-014-1795-6.
[41] Xiaofang Yuan, Yongzhong Xiang, and Yuqing He: Parameter extraction of solar cell models using mutative-scale parallel chaos optimization algorithm. Solar Energy, 108 (2014), 238–251, DOI: 10.1016/j.solener.2014.07.013.
[42] Alireza Askarzadeh and Alireza Rezazadeh: Artificial bee swarm optimization algorithm for parameters identification of solar cell models. Applied Energy, 102 (2013), 943–949, DOI: 10.1016/j.apenergy.2012.09.052.
[43] Santhan Kumar Cherukuri and Srinivasa Rao Rayapudi: Enhanced grey wolf optimizer based MPPT algorithm of PV system under partial shaded condition. International Journal of Renewable Energy Development, 6(3), (2017), 203–212, DOI: 10.14710/ijred.6.3.203-212.
[44] Adeel Feroz Mirza, Qiang Ling, M. Yaqoob Javed, and Majad Man- soor: Novel MPPT techniques for photovoltaic systems under uniform irradiance and Partial shading. Solar Energy, 184 (2019), 628–648, DOI: 10.1016/j.solener.2019.04.034.
Przejdź do artykułu

Autorzy i Afiliacje

Khadidja Bentata
1
Ahmed Mohammedi
2 3
Tarak Benslimane
4 5

  1. Laboratory Materials and Sustainable Development (LMDD), Electrical Engineering Department, Faculty of Science and Applied Sciences, University of Bouira, Algeria
  2. Electrical Engineering Department, Faculty of Science and Applied Sciences, University of Bouira, Algeria
  3. LTII Laboratory, University of Bejaia, Algeria
  4. Electrical Engineering Department, University of M’sila, Algeria
  5. SGRE Laboratory, University of Béchar, Algeria
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

This paper presents how Q-learning algorithm can be applied as a general-purpose selfimproving controller for use in industrial automation as a substitute for conventional PI controller implemented without proper tuning. Traditional Q-learning approach is redefined to better fit the applications in practical control loops, including new definition of the goal state by the closed loop reference trajectory and discretization of state space and accessible actions (manipulating variables). Properties of Q-learning algorithm are investigated in terms of practical applicability with a special emphasis on initializing of Q-matrix based only on preliminary PI tunings to ensure bumpless switching between existing controller and replacing Q-learning algorithm. A general approach for design of Q-matrix and learning policy is suggested and the concept is systematically validated by simulation in the application to control two examples of processes exhibiting first order dynamics and oscillatory second order dynamics. Results show that online learning using interaction with controlled process is possible and it ensures significant improvement in control performance compared to arbitrarily tuned PI controller.
Przejdź do artykułu

Bibliografia

[1] H. Boubertakh, S. Labiod, M. Tadjine and P.Y. Glorennec: Optimization of fuzzy PID controllers using Q-learning algorithm. Archives of Control Sciences, 18(4), (2008), 415–435
[2] I.Carlucho, M. De Paula, S.A. Villar and G.G.Acosta: Incremental Qlearning strategy for adaptive PID control of mobile robots. Expert Systems With Applications, 80, (2017), 183–199, DOI: 10.1016/j.eswa.2017.03.002.
[3] K. Delchev: Simulation-based design of monotonically convergent iterative learning control for nonlinear systems. Archives of Control Sciences, 22(4), (2012), 467–480.
[4] M. Jelali: An overview of control performance assessment technology and industrial applications. Control Eng. Pract., 14(5), (2006), 441–466, DOI: 10.1016/j.conengprac.2005.11.005.
[5] M. Jelali: Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance. Springer-Verlag London, (2013)
[6] H.-K. Lam, Q. Shi, B. Xiao, and S.-H. Tsai: Adaptive PID Controller Based on Q-learning Algorithm. CAAI Transactions on Intelligence Technology, 3(4), (2018), 235–244, DOI: 10.1049/trit.2018.1007.
[7] D. Li, L. Qian, Q. Jin, and T. Tan: Reinforcement learning control with adaptive gain for a Saccharomyces cerevisiae fermentation process. Applied Soft Computing, 11, (2011), 4488–4495, DOI: 10.1016/j.asoc.2011.08.022.
[8] M.M. Noel and B.J. Pandian: Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach. Applied Soft Computing, 23, (2014), 444–451, DOI: 10.1016/j.asoc.2014.06.037.
[9] T. Praczyk: Concepts of learning in assembler encoding. Archives of Control Sciences, 18(3), (2008), 323–337.
[10] M.B. Radac and R.E. Precup: Data-driven model-free slip control of antilock braking systems using reinforcement Q-learning. Neurocomputing, 275, (2017), 317–327, DOI: 10.1016/j.neucom.2017.08.036.
[11] A.K. Sadhu and A. Konar: Improving the speed of convergence of multi-agent Q-learning for cooperative task-planning by a robot-team. Robotics and Autonomous Systems, 92, (2017), 66–80, DOI: 10.1016/j.robot.2017.03.003.
[12] N. Sahebjamnia, R. Tavakkoli-Moghaddam, and N. Ghorbani: Designing a fuzzy Q-learning multi-agent quality control system for a continuous chemical production line – A case study. Computers & Industrial Engineering, 93, (2016), 215–226, DOI: 10.1016/j.cie.2016.01.004.
[13] K. Stebel: Practical aspects for the model-free learning control initialization. in Proc. of 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR), Poland, (2015), DOI: 10.1109/MMAR.2015.7283918.
[14] R.S. Sutton and A.G. Barto: Reinforcement learning: An Introduction, MIT Press, (1998)
[15] S. Syafiie, F. Tadeo, and E. Martinez: Softmax and "-greedy policies applied to process control. IFAC Proceedings, 37, (2004), 729–734, DOI: 10.1016/S1474-6670(16)31556-2.
[16] S. Syafiie, F. Tadeo, and E. Martinez: Model-free learning control of neutralization process using reinforcement learning. Engineering Applications of Artificial Intelligence, 20, (2007), 767–782, DOI: 10.1016/j.engappai.2006.10.009.
[17] S. Syafiie, F. Tadeo, and E. Martinez: Learning to control pH processes at multiple time scales: performance assessment in a laboratory plant. Chemical Product and Process Modeling, 2(1), (2007), DOI: 10.2202/1934- 2659.1024.
[18] S. Syafiie, F. Tadeo, E. Martinez, and T. Alvarez: Model-free control based on reinforcement learning for a wastewater treatment problem. Applied Soft Computing, 11, (2011), 73–82, DOI: 10.1016/j.asoc.2009.10.018.
[19] P. Van Overschee and B. De Moor: RAPID: The End of Heuristic PID Tuning. IFAC Proceedings, 33(4), (2000), 595–600, DOI: 10.1016/S1474- 6670(16)38308-8.
[20] M. Wang, G. Bian, and H. Li: A new fuzzy iterative learning control algorithm for single joint manipulator. Archives of Control Sciences, 26(3), (2016), 297–310. DOI: 10.1515/acsc-2016-0017.
[21] Ch.J.C.H. Watkins and P. Dayan: Technical Note: Q-learning. Machine Learning, 8, (1992), 279–292, DOI: 10.1023/A:1022676722315.
Przejdź do artykułu

Autorzy i Afiliacje

Jakub Musial
1
Krzysztof Stebel
1
Jacek Czeczot
1

  1. Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, Department of Automatic Control and Robotics, 44-100 Gliwice, ul. Akademicka 16, Poland
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

The hybridization of a recently suggested Harris hawk’s optimizer (HHO) with the traditional particle swarm optimization (PSO) has been proposed in this paper. The velocity function update in each iteration of the PSO technique has been adopted to avoid being trapped into local search space with HHO. The performance of the proposed Integrated HHO-PSO (IHHOPSO) is evaluated using 23 benchmark functions and compared with the novel algorithms and hybrid versions of the neighbouring standard algorithms. Statistical analysis with the proposed algorithm is presented, and the effectiveness is shown in the comparison of grey wolf optimization (GWO), Harris hawks optimizer (HHO), barnacles matting optimization (BMO) and hybrid GWO-PSO algorithms. The comparison in convergence characters with the considered set of optimization methods also presented along with the boxplot. The proposed algorithm is further validated via an emerging engineering case study of controller parameter tuning of power system stability enhancement problem. The considered case study tunes the parameters of STATCOM and power system stabilizers (PSS) connected in a sample power network with the proposed IHHOPSO algorithm. A multi-objective function has been considered and different operating conditions has been investigated in this papers which recommends proposed algorithm in an effective damping of power network oscillations.
Przejdź do artykułu

Bibliografia

[1] M. Crepinsek, S.-H. Liu, and L. Mernik: A note on teaching–learningbased optimization algorithm. Information Sciences, 212 (2012), 79–93, DOI: 10.1016/j.ins.2012.05.009.
[2] Anita and A. Yadav: AEFA: Artificial electric field algorithm for global optimization. Swarm and Evolutionary Computation, 48 (2019), 93–108, DOI: 10.1016/j.swevo.2019.03.013.
[3] R. Devarapalli and B. Bhattacharyya: A hybrid modified grey wolf optimization-sine cosine algorithm-based power system stabilizer parameter tuning in a multimachine power system. Optimal Control Applications and Methods, 41(4), (2020), 1143-1159, DOI: 10.1002/oca.2591.
[4] M. Jain, V. Singh, and A. Rani: A novel nature-inspired algorithm for optimization: Squirrel search algorithm, Swarmand Evolutionary Computation, 44 (2019), 148–175, DOI: 10.1016/j.swevo.2018.02.013.
[5] A.E. Eiben and J.E. Smith: What is an Evolutionary Algorithm? In Introduction to Evolutionary Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2015, 25–48, DOI: 10.1007/978-3-662-44874-8_3.
[6] A. Kaveh and M. Khayatazad: A new meta-heuristic method: Ray Optimization. Computers & Structures, 112–113, (2012), 283–294, DOI: 10.1016/j.compstruc.2012.09.003.
[7] P.J.M. van Laarhoven and E.H.L. Aarts: Simulated annealing. In Simulated Annealing: Theory and Applications, P.J.M. van Laarhoven and E.H.L. Aarts, Eds. Dordrecht: Springer Netherlands, 1987, 7–15, DOI: 10.1007/978-94-015-7744-1_2.
[8] Agenetic algorithm tutorial. SpringerLink. https://link.springer.com/article/10.1007/BF00175354 (accessed Mar. 20, 2020).
[9] J. Kennedy and R. Eberhart: Particle Swarm Optimization. Proc. of ICNN’95 International Conference on Neural Networks, 4 (1995), 1942– 1948.
[10] M. Neshat, G. Sepidnam, M. Sargolzaei, and A.N. Toosi: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artificial Intelligence Review, 42(4), (2014), 965–997, DOI: 10.1007/s10462-012-9342-2.
[11] M. Dorigo, M. Birattari, and T. Stutzle: Ant colony optimization. IEEE Computational Intelligence Magazine, 1(4), (2006), 28–39, DOI: 10.1109/ MCI.2006.329691.
[12] M. Roth and S. Wicker: Termite: ad-hoc networking with stigmergy. In GLOBECOM’03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489), 5 (2003), 2937–2941, DOI: 10.1109/GLOCOM.2003.1258772.
[13] D. Karaboga and B. Akay: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation, 214(1), (2009), 108– 132, DOI: 10.1016/j.amc.2009.03.090.
[14] A. Mucherino and O. Seref: Monkey search: a novel metaheuristic search for global optimization. AIP Conference Proceedings, 953(1), (2007), 162– 173, DOI: 10.1063/1.2817338.
[15] E.Atashpaz-Gargari and C. Lucas: Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. In 2007 IEEE Congress on Evolutionary Computation, (2007), 4661–4667, DOI: 10.1109/CEC.2007.4425083.
[16] D. Simon: Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12(6), (2008), 702–713, DOI: 10.1109/TEVC.2008.919004.
[17] X.-S. Yang: Firefly algorithm. Stochastic, test, functions and design optimisation. arXiv:1003.1409 [math], Mar. 2010, Accessed: Mar. 20, 2020. [Online]. Available: http://arxiv.org/abs/1003.1409.
[18] K.M.Gates and P.C.M. Molenaar: Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), (2012), 310–319, DOI: 10.1016/j.neuroimage.2012.06.026.
[19] E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi: GSA: A gravitational search algorithm. Information Sciences, 179(13), (2009), 2232–2248, DOI: 10.1016/j.ins.2009.03.004.
[20] Y. Tan andY. Zhu: Fireworks Algorithm for Optimization. In: TanY., ShiY., Tan K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, 6145, Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13495-1_44.
[21] X.-S. Yang: Bat algorithm for multi-objective optimisation. arXiv: 1203. 6571 [math], Mar. 2012, Accessed: Mar. 20, 2020. [Online]. Available: http://arxiv.org/abs/1203.6571.
[22] LingWang, Xiao-long Zheng, and Sheng-yaoWang:Anovel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem. Knowledge-Based Systems, 48 17–23, (2013), DOI: 10.1016/j.knosys.2013.04.003.
[23] X.-S. Yang: Flower Pollination Algorithm for Global Optimization. In Unconventional Computation and Natural Computation, Berlin, Heidelberg, 2012, 240–249, DOI: 10.1007/978-3-642-32894-7_27.
[24] G.-G. Wang, L. Guo, A.H. Gandomi, G.-S. Hao, and H. Wang: Chaotic Krill Herd algorithm. Information Sciences, 274 (2014), 17–34, DOI: 10.1016/j.ins.2014.02.123.
[25] A. Kaveh and N. Farhoudi: A new optimization method: Dolphin echolocation. Advances in Engineering Software, 59 (2013), 53–70, DOI: 10.1016/ j.advengsoft.2013.03.004.
[26] S. Mirjalili, S.M. Mirjalili, and A. Lewis: GreyWolf optimizer. Advances in Engineering Software, 69 (2014), 46–61, DOI: 10.1016/j.advengsoft.2013.12.007.
[27] A. Hatamlou: Black hole: A new heuristic optimization approach for data clustering. Information Sciences, 222 (2013), 175–184, DOI: 10.1016/ j.ins.2012.08.023.
[28] A. Sadollah, A. Bahreininejad, H. Eskandar and M. Hamdi: Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problem. Applied Soft Computing, 13(5), (2013), 2592–2612, DOI: 10.1016/j.asoc.2012.11.026.
[29] S. Mirjalili: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), (2016), 1053–1073, DOI: 10.1007/s00521-015-1920-1.
[30] S. Mirjalili: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89 (2015), 228–249, DOI: 10.1016/j.knosys.2015.07.006.
[31] F.A. Hashim, E.H. Houssein, M.S. Mabrouk, W. Al-Atabany, and S. Mirjalili: Henry gas solubility optimization: A novel physics-based algorithm. Future Generation Computer Systems, 101 (2019), 646–667, DOI: 10.1016/j.future.2019.07.015.
[32] S. Mirjalili: The ant lion optimizer. Advances in Engineering Software, 83 (2015), 80–98, DOI: 10.1016/j.advengsoft.2015.01.010.
[33] H. Shareef, A.A. Ibrahim, and A.H. Mutlag: Lightning search algorithm. Applied Soft Computing, 36 (2015), 315–333, DOI: 10.1016/j.asoc.2015.07.028.
[34] S.A. Uymaz, G. Tezel, and E. Yel: Artificial algae algorithm (AAA) for nonlinear global optimization. Applied Soft Computing, 31 (2015), 153–171, DOI: 10.1016/j.asoc.2015.03.003.
[35] M.D. Li, H. Zhao, X.W. Weng, and T. Han: A novel nature-inspired algorithm for optimization: Virus colony search. Advances in Engineering Software, 92 (2016), 65–88, DOI: 10.1016/j.advengsoft.2015.11.004.
[36] O. Abedinia, N. Amjady, and A. Ghasemi: A new metaheuristic algorithm based on shark smell optimization. Complexity, 21(5), (2016), 97–116, DOI: 10.1002/cplx.21634.
[37] S. Mirjalili, S.M. Mirjalili, and A. Hatamlou: Multi-Verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), (2016), 495–513, DOI: 10.1007/s00521-015-1870-7.
[38] S. Mirjalili and A. Lewis: The whale optimization algorithm. Advances in Engineering Software, 95 (2016), 51–67, DOI: 10.1016/j.advengsoft. 2016.01.008.
[39] A. Askarzadeh: A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers and Structures, 169 (2016), 1–12, DOI: 10.1016/j.compstruc.2016.03.001.
[40] T. Wu, M. Yao, and J. Yang: Dolphin swarm algorithm. Frontiers of Information Technology & Electronic Engineering, 17(8), (2016), 717–729, DOI: 10.1631/FITEE.1500287.
[41] S. Mirjalili: SCA: A sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96 (2016), 120–133, DOI: 10.1016/j.knosys.2015.12.022.
[42] A. Kaveh and A. Dadras: A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110, (2017), 69–84, DOI: 10.1016/j.advengsoft.2017.03.014.
[43] M.M. Mafarja, I. Aljarah, A. Asghar Heidari, A.I. Hammouri, H. Faris, Ala’M. Al-Zoubi, and S. Mirjalili: Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-Based Systems, 145 (2018), 25–45, DOI: 10.1016/j.knosys.2017.12.037.
[44] A. Tabari and A. Ahmad: A new optimization method: Electro-search algorithm. Computers and Chemical Engineering, 103 (2017), 1–11, DOI: 10.1016/j.compchemeng.2017.01.046.
[45] G. Dhiman and V. Kumar: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114 (2017), 48–70, DOI: 10.1016/j.advengsoft. 2017.05.014.
[46] S.-A. Ahmadi: Human behavior-based optimization: a novel metaheuristic approach to solve complex optimization problems. Neural Comput and Applications, 28(S1), (2017), 233–244, DOI: 10.1007/s00521-016-2334-4.
[47] A.F. Nematollahi, A. Rahiminejad, and B. Vahidi: A novel physical based meta-heuristic optimization method known as lightning attachment procedure optimization. Applied Soft Computing, 59 (2017), 596–621, DOI: 10.1016/j.asoc.2017.06.033.
[48] R.A. Ibrahim, A.A. Ewees, D. Oliva, M. Abd Elaziz, and S. Lu: Improved salp swarm algorithm based on particle swarm optimization for feature selection. Journal of Ambient Intelligence and Humanized Computing, 10(8), (2019), 3155–3169, DOI: 10.1007/s12652-018-1031-9.
[49] E. Jahani and M. Chizari: Tackling global optimization problems with a novel algorithm – Mouth brooding fish algorithm. Applied Soft Computing, 62 (2018), 987–1002, DOI: 10.1016/j.asoc.2017.09.035.
[50] X. Qi, Y. Zhu, and H. Zhang: A new meta-heuristic butterfly-inspired algorithm. Journal of Computational Science, 23 (2017), 226–239, DOI: 10.1016/j.jocs.2017.06.003.
[51] S. Mirjalili: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89 (2015), 228–249, DOI: 10.1016/j.knosys.2015.07.006.
[52] M. Dorigo, V. Maniezzo, and A. Colorni: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), (1996), 29–41, DOI: 10.1109/3477.484436.
[53] S. Mirjalili and S.Z.M. Hashim: A new hybrid PSOGSA algorithm for function optimization. In 2010 International Conference on Computer and Information Application, (2010), 374–377, DOI: 10.1109/ICCIA.2010.6141614.
[54] F.A. Senel, F. Gokce, A.S. Yuksel, and T. Yigit: A novel hybrid PSO– GWO algorithm for optimization problems. Engineering with Computers, 35(4), 1359–1373, DOI: 10.1007/s00366-018-0668-5.
[55] D.T. Bui, H. Moayedi, B. Kalantar, and A. Osouli: Harris hawks optimization: A novel swarm intelligence technique for spatial assessment of landslide susceptibility. Sensors, 19(14), (2019), 3590, DOI: 10.3390/s19163590.
[56] H. Chen, S. Jiao, M.Wang, A.A. Heidari, and X. Zhao: Parameters identification of photovoltaic cells and modules using diversification-enriched Harris hawks optimization with chaotic drifts. Journal of Cleaner Production, 244 (2020), p. 118778, DOI: 10.1016/j.jclepro.2019.118778.
[57] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen: Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97 (2019), 849–872, DOI: 10.1016/ j.future.2019.02.028.
[58] M. Jamil and X.-S. Yang: A literature survey of benchmark functions for global optimization problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), (2013), 150, DOI: 10.1504/IJMMNO.2013.055204.
[59] A. Kaveh and S. Talatahari: A novel heuristic optimization method: charged system search. Acta Mechanica, 213(3–4), (2010), 267–289, DOI: 10.1007/s00707-009-0270-4.
[60] J. Luo and B. Shi: Ahybrid whale optimization algorithm based on modified differential evolution for global optimization problems. Applied Intelligence, 49(5), (2000), 1982–2000, DOI: 10.1007/s10489-018-1362-4.
[61] A.A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen: Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97 (2019), 849–872, DOI: 10.1016/ j.future.2019.02.028.
[62] P. Pruski and S. Paszek: Location of generating units most affecting the angular stability of the power system based on the analysis of instantaneous power waveforms. Archives of Control Sciences, 30(2), (2020), 273–293, DOI: 10.24425/acs.2020.133500.
[63] M.M. Hossain and A.Z. Khurshudyan: Heuristic control of nonlinear power systems: Application to the infinite bus problem. Archives of Control Sciences, 29(2), (2019), 279–288, DOI: 10.24425/acs.2019.129382.
[64] R. Devarapalli and B. Bhattacharyya:Aframework for H2=H? synthesis in damping power network oscillations with STATCOM. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 44 (2020), 927-948, DOI: 10.1007/s40998-019-00278-4.
[65] G. Gurrala and I. Sen: Power system stabilizers design for interconnected power systems. IEEE Transactions on Power Systems, 25(2), (2010), 1042– 1051, DOI: 10.1109/TPWRS.2009.2036778.
[66] R.K. Varma: Introduction to FACTS controllers. In 2009 IEEE/PES Power Systems Conference and Exposition, (2009), 1–6, DOI: 10.1109/PSCE.2009.4840114.
[67] P. Kundur: Power System Stability and Control. Tata McGraw-Hill Education, 1994.
[68] M. Belazzoug, M. Boudour, and K. Sebaa: FACTS location and size for reactive power system compensation through the multi-objective optimization. Archives of Control Sciences, 20(4), (2010), 473–489, DOI: 10.2478/v10170-010-0027-2
Przejdź do artykułu

Autorzy i Afiliacje

Ramesh Devarapalli
1
ORCID: ORCID
Vikash Kumar
1

  1. Department of Electrical Engineering, B.I.T. Sindri, Dhanbad, Jharkhand, India
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

Poles and zeros assignment problem by state feedbacks in positive continuous-time and discrete-time systems is analyzed. It is shown that in multi-input multi-output positive linear systems by state feedbacks the poles and zeros of the transfer matrices can be assigned in the desired positions. In the positive continuous-time linear systems the feedback gain matrix can be chosen as a monomial matrix so that the poles and zeros of the transfer matrices have the desired values if the input matrix B is monomial. In the positive discrete-time linear systems to solve the problem the matrix B can be chosen monomial if and only if in every row and every column of the n x n system matrix A the sum of n-1 its entries is less than one. Key words: assignment, pole, zero, transfer matrix, linear, positive, system, state feedback
Przejdź do artykułu

Bibliografia

[1] E. Antsaklis and A. Michel: Linear Systems. Birkhauser, Boston, 2006.
[2] L. Farina and S. Rinaldi: Positive Linear Systems: Theory and Applications. J. Wiley & Sons, New York, 2000.
[3] T. Kaczorek: Linear Control Systems, vol. 2. Research Studies Press LTD., J. Wiley, New York, 1992.
[4] T. Kaczorek: Positive 1D and 2D Systems. London, UK, Springer-Verlag, 2002.
[5] T. Kaczorek: Selected Problems of Fractional Systems Theory. Berlin, Germany, Springer-Verlag, 2011.
[6] T. Kaczorek and K. Rogowski: Fractional Linear Systems and Electrical Circuits, Studies in Systems, Decision and Control, Vol. 13. Springer, 2015.
[7] T. Kailath: Linear systems. Prentice Hall, Englewood Cliffs, New York, 1980.
[8] R.E. Kalman: Mathematical description of linear systems. J. SIAM Control, 1(2), (1963), 152–192, DOI: 10.1137/0301010.
[9] R.E. Kalman: On the general theory of control systems. Proc. First International Congress on Automatic Control, London, UK, Butterworth, (1960), 481–493,
[10] J. Klamka: Controllability of Dynamical Systems. Kluwer, Acadenic Publ., Dordrecht 1991.
[11] H. Rosenbrock: State-Space and Multivariable Theory. New York, USA, J. Wiley, 1970.
[12] S.M. Zak: Systems and Control. New York, Oxford University Press, 2003.
Przejdź do artykułu

Autorzy i Afiliacje

Tadeusz Kaczorek
1

  1. Białystok University of Technology, Faculty of Electrical Engineering, Wiejska 45D, 15-351 Białystok, Poland
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

The basic objective of the research is to construct a difference model of the melt motion. The existence of a solution to the problem is proven in the paper. It is also proven the convergence of the difference problem solution to the original problem solution of the melt motion. The Rothe method is implemented to study the Navier–Stokes equations, which provides the study of the boundary value problems correctness for a viscous incompressible flow both numerically and analytically.
Przejdź do artykułu

Bibliografia

[1] R. Lakshminarayana, K. Dadzie, R. Ocone, M. Borg, and J. Reese: Recasting Navier–Stokes equations. Journal of Physics Communications, 3(10), (2019), 13–18, DOI: 10.1088/2399-6528/ab4b86.
[2] S.Sh. Kazhikenova, S.N. Shaltaqov, D. Belomestny, and G.S. Shai- hova: Finite difference method implementation for Numerical integration hydrodynamic equations melts. Eurasian Physical Technical Journal, 17(33), (2020), 50–56.
[3] C. Bardos: A basic example of non linear equations: The Navier– Stokes equations. Mathematics: Concepts and Foundations, III (2002), http://www.eolss.net/sample-chapters/c02/e6-01-06-02.pdf.
[4] J.XuandW.Yu:ReducedNavier–Stokes equations with streamwise viscous diffusion and heat conduction terms. AIAA Pap., 1441 (1990), 1–6, DOI: 10.2514/6.1990-1441.
[5] Y. Seokwan and K. Dochan: Three-dimensional incompressible Navier– Stokes solver using lower-upper symmetric Gauss–Seidel algorithm. AIAA Journal, 29(6), (1991), 874–875, DOI: 10.2514/3.10671.
[6] P.M. Gresho: Incompressible fluid dynamics: some fundamental formulation issues. Annual Review of Fluid Mechanics, 23, (1991), 413–453, DOI: 10.1146/annurev.fl.23.010191.002213.
[7] S.E. Rogers, K. Dochan, and K. Cetin: Steady and unsteady solutions of the incompressible Navier–Stokes equations. AIAA Journal, 29(4), (1991), 603–610, DOI: 10.2514/3.10627.
[8] S. Masayoshi, T. Hiroshi, S. Nobuyuki, and N. Hidetoshi: Numerical simulation of three-dimensional viscous flows using the vector potential method. JSME International Journal, 34(2), (1991), 109–114, DOI: 10.1299/jsmeb1988.34.2_109.
[9] E. Sciubba: A variational derivation of the Navier–Stokes equations based on the exergy destruction of the flow. Journal of Mathematical and Physical Sciences, 25(1), (1991), 61–68.
[10] A. Bouziani and R. Mechri: The Rothe’s method to a parabolic integrodifferential equation with a nonclassical boundary conditions. International Journal of Stochastic Analysis, Article ID 519684, (2010), DOI: 10.1155/2010/519684.
[11] N. Merazga and A. Bouziani: Rothe time-discretization method for a nonlocal problem arising in thermoelasticity. Journal of Applied Mathematics and Stochastic Analysis, 2005(1), (2005), 13–28, DOI: 10.1080/00036818908839869.
[12] T.A. Barannyk, A.F. Barannyk, and I.I. Yuryk: Exact solutions of the nonliear equation. Ukrains’kyi Matematychnyi Zhurnal, 69(9), (2017), 1180–1186, http://umj.imath.[K]iev.ua/index.php/umj/article/view/1768.
[13] N.B. Iskakova, A.T. Assanova, and E.A. Bakirova: Numerical method for the solution of linear boundary-value problem for integrodifferential equations based on spline approximations. Ukrains’kyi Matematychnyi Zhurnal, 71(9), (2019), 1176–91, http://umj.imath.[K]iev.ua/index.php/ umj/article/view/1508.
[14] S.L. Skorokhodov and N.P. Kuzmina: Analytical-numerical method for solving an Orr-Sommerfeld type problem for analysis of instability of ocean currents. Zh. Vychisl. Mat. Mat. Fiz., 58(6), (2018), 1022–1039, DOI: 10.7868/S0044466918060133.
Przejdź do artykułu

Autorzy i Afiliacje

Saule Sh. Kazhikenova
1
ORCID: ORCID
Sagyndyk N. Shaltakov
1
ORCID: ORCID
Bekbolat R. Nussupbekov
2
ORCID: ORCID

  1. Karaganda Technical University, Kazakhstan
  2. Karaganda University E.A. Buketov, Kazakhstan
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

This paper studies an evacuation problem described by a leader-follower model with bounded confidence under predictive mechanisms. We design a control strategy in such a way that agents are guided by a leader, which follows the evacuation path. The proposed evacuation algorithm is based on Model Predictive Control (MPC) that uses the current and the past information of the system to predict future agents’ behaviors. It can be observed that, with MPC method, the leader-following consensus is obtained faster in comparison to the conventional optimal control technique. The effectiveness of the developed MPC evacuation algorithm with respect to different parameters and different time domains is illustrated by numerical examples.
Przejdź do artykułu

Bibliografia

[1] H. Abdelgawad and B. Abdulhai: Emergency evacuation planning as a network design problem: A critical review. Transportation Letters: The International Journal of Transportation Research, 1 (2009), 41–58, DOI: 10.3328/TL.2009.01.01.41-58.
[2] R. Alizadeh: A dynamic cellular automaton model for evacuation process with obstacles, Safety Science, 49(2), (2011), 315–323, DOI: 10.1016/j.ssci.2010.09.006.
[3] R. Almeida, E. Girejko, L. Machado, A.B. Malinowska, and N. Mar- tins: Application of predictive control to the Hegselmann-Krause model, Mathematical Methods in the Applied Sciences, 41(18), (2018), 9191–9202, DOI: 10.10022Fmma.5132.
[4] B. Aulbach and S. Hilger: A unified approach to continuous and discrete dynamics, ser. Colloq. Math. Soc. Janos Bolyai, vol. 53, North-Holland, Amsterdam, 1990.
[5] H. Bi and E. Gelenbe: A survey of algorithms and systems for evacuating people in confined spaces, Electronics, 2019 8(6), (2019), 711, DOI: 10.3390/electronics8060711.
[6] V.D. Blondel, J.M. Hendrickx, and J.N. Tsitsiklis: On Krause’s multiagent consensus model with state-dependent connectivity, IEEE Transactions on Automatics Control, vol. 54(11), (2009), 2586–2597, DOI: 10.1109/TAC.2009.2031211.
[7] V.D. Blondel, J.M. Hendrickx, and J.N. Tsitsiklis: Continuous-time average-preserving opinion dynamics with opinion-dependent communications, SIAM Journal on Control and Optimization, vol. 48(8), (2010), 5214–5240, DOI: 10.1137/090766188.
[8] M. Bohner and A. Peterson: Dynamic equations on time scales, Boston, MA: Birkhäuser Boston, 2001.
[9] R.M. Colombo and M. D. Rosini: Pedestrian flows and non-classical shocks, Mathematical Methods in the Applied Sciences, 28(13), (2005), 1553–1567, DOI: 10.1002/mma.624.
[10] E. Girejko, L. Machado, A.B. Malinowska, and N. Martins: Krause’s model of opinion dynamics on isolated time scales, Mathematical Methods in the Applied Sciences, 39 (2016), 5302–5314, DOI: 10.1002/mma.3916.
[11] R. Hegselmann and U. Krause: Opinion dynamics and bounded confidence models, analysis, and simulation, Journal of Artificial Societies and Social Simulation, 5(3), (2002), http://jasss.soc.surrey.ac.uk/5/3/2.html.
[12] D. Helbing and P. Molnar: Social force model for pedestrian dynamics, Physical Review E, 51(5), (1995), 4282–4286, DOI: 10.1103/Phys-RevE.51.4282.
[13] R. Hilscher and V. Zeidan:Weak maximum principle and accessory problem for control problems on time scales, Nonlinear Analysis, 70(9), (2009), 3209–3226, DOI: 10.1016/j.na.2008.04.025.
[14] L. Huang, S.C.Wong, M. Zhang, C.-W. Shu, andW.H.K. Lam: Revisiting Hughes’ dynamics continuum model for pedestrian flow and the development of an efficient solution algorithm, Transportation Research Part B: Methodological, 43(1), (2009), 127–141, DOI: 10.1016/j.trb.2008.06.003.
[15] R.L. Hughes: A continuum theory for the flow of pedestrians, Transportation Research Part B: Methodological, 36(6), (2002), 507–535, DOI: 10.1016/S0191-2615(01)00015-7.
[16] R. Lohner: On the modeling of pedestrian motion, Applied Mathematical Modeling, 34(2), (2010), 366–382, DOI: 10.1016/j.apm.2009.04.017.
[17] S.J. Qin and T.A. Badgwell: An Overview of Nonlinear Model Predictive Control Applications, Allgöwer F., Zheng A. ed., ser. Nonlinear Model Predictive Control. Progress in Systems and Control Theory. Birkhäuser, Basel, 2000, vol. 26, pp. 369–392.
[18] S. Wojnar, T. Poloni, P. Šimoncic, B. Rohal’-Ilkiv, M. Honek (and) J. Csambál: Real-time implementation of multiple model based predictive control strategy to air/fuel ratio of a gasoline engine. Archives of Control Sciences, 23(1), (2013), 93–106.
[19] S. Daniar, M. Shiroei and R. Aazami: Multivariable predictive control considering time delay for load-frequency control in multi-area power systems. Archives of Control Sciences, 26(4), (2016), 527–549, DOI: 10.1515/acsc-2016-0029.
[20] Y. Yang, D.V. Dimarogonas, and X. Hu: Optimal leader-follower control for crowd evacuation, Proc. 52nd IEEE Conf. Decision Control (CDC), (2013), 2769–2774, DOI: 10.1109/CDC.2013.6760302.
[21] Z. Zainuddin and M. Shuaib: Modification of the decision-making capability in the social force model for the evacuation process, Transport Theory and Statistical Physics, 39(1), (2011), 47–70, DOI: 10.1080/00411450.2010.529979.
[22] H.-T. Zhang, M.Z. Chen, G.-B. Stan, and T. Zhou: Ultrafast consensus via predictive mechanisms, Europhysics Letters, 83, (2008), no. 40003.
[23] H.-T. Zhang, M.Z. Chen, G.-B. Stan, T. Zhou, and J.M.Maciejowski: Collective behaviour coordination with predictive mechanisms, IEEE Circuits Systems Magazine, 8, (2008) 67–85, DOI: 10.1109/MCAS.2008.928446.
[24] L. Zhang, J. Wang, and Q. Shi: Multi-agent based modeling and simulating for evacuation process in stadium, Journal of Systems Science and Complexity, 27(3), (2014), 430–444, DOI: 10.1007/s11424-014-3029-5.
[25] Y. Zheng, B. Jia, X.-G. Li, and N. Zhu: Evacuation dynamics with fire spreading based on cellular automaton, Physica A: Statistical Mechanics and Its Applications, 390(18-19), (2011), 3147–3156, DOI: 10.1016/j.physa.2011.04.011.
Przejdź do artykułu

Autorzy i Afiliacje

Ricardo Almeida
1
Ewa Girejko
2
Luís Machado
3 4
Agnieszka B. Malinowska
2
Natália Martins
1

  1. Center for Research and Development in Mathematics and Applications (CIDMA), Department of Mathematics, University of Aveiro, 3810–193 Aveiro, Portugal
  2. Faculty of Computer Science, Bialystok University of Technology, 15-351 Białystok, Poland
  3. Institute of Systems and Robotics, DEEC – UC, 3030-290 Coimbra, Portugal
  4. Department of Mathematics, University of Trás-os-Montes e Alto Douro (UTAD), 5000-801 Vila Real, Portugal
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

In modern society, people concern more about the evaluation of medical service quality. Evaluation of medical service quality is helpful for medical service providers to supervise and improve their service quality. Also, it will help the public to understand the situation of different medical providers. As a multi-criteria decision-making (MCDM) problem, evaluation of medical service quality can be effectively solved by aggregation operators in interval-valued q-rung dual hesitant fuzzy (IVq-RDHF) environment. Thus, this paper proposes interval-valued q-rung dual hesitant Maclaurin symmetric mean (IVq-RDHFMSM) operator and interval-valued q-rung dual hesitant weighted Maclaurin symmetric mean (IVq-RDHFWMSM) operator. Based on the proposed IVq-RDHFWMSM operator, this paper builds a novel approach to solve the evaluation problem of medical service quality including a criteria framework for the evaluation of medical service quality and a novel MCDM method. What’s more, aiming at eliminating the discordance between decision information and weight vector of criteria determined by decisionmakers (DMs), this paper proposes the concept of cross-entropy and knowledge measure in IVq-RDHF environment to extract weight vector from DMs’ decision information. Finally, this paper presents a numerical example of the evaluation of medical service for hospitals to illustrate the availability of the novel method and compares our method with other MCDM methods to demonstrate the superiority of our method. According to the comparison result, our method has more advantages than other methods.
Przejdź do artykułu

Bibliografia

[1] C. Teng, C. Ing, H. Chang, and K. Chung: Development of service quality scale for surgical hospitalization. Journal of the Formosan Medical Association, 106(6), (2007), 475–484, DOI: 10.1016/S0929-6646(09)60297-7.
[2] I. Otay, B. Öztaysi, S. Çevik, and C. Kahraman: Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 33 (2017), 90– 106, DOI: 10.1016/j.knosys.2017.06.028.
[3] J. Shieh, H. Wu, and K. Huang: A DEMATEL method in identifying key success factors of hospital service quality. Knowledge Based Systems, 23(3), (2010), 277–282, DOI: 10.1016/j.knosys.2010.01.013.
[4] M.L. Mccarthy, R. Ding, and S.L. Zeger: A randomized controlled trial of the effect of service delivery information on patient satisfaction in an emergency department fast track. Academic Emergency Medicine, 18(7), (2011), 674–685, DOI: 10.1111/j.1553-2712.2011.01119.x.
[5] L. Fei, J. Lu, and Y. Feng: An extended best-worst multi-criteria decisionmaking method by belief functions and its applications in hospital service evaluation. Computers&Industrial Engineering, 142, (2020), 106355, DOI: 10.1016/j.cie.2020.106355.
[6] E.K. Zavadskas, Z. Turskis, and S. Kildien˙e: State of art surveys of overviews on MCDM/MADM methods. Technological and Economic Development of Economy, 20(1), (2014), 165–179, DOI: 10.3846/20294913.2014.892037.
[7] Y. Xing, R. Zhang, M. Xia,and J. Wang: Generalized point aggregation operators for dual hesitant fuzzy information. Journal of Intelligent and Fuzzy Systems, 33(1), (2017), 515–527, DOI: 10.3233/JIFS-161922.
[8] F. Zhang, S.Wang, J. Sun, J. Ye, and G.K. Liew:Novel parameterized score functions on interval-valued intuitionistic fuzzy sets with three fuzziness measure indexes and their application. IEEE Access, 7, (2018), 8172–8180, DOI: 10.1109/ACCESS.2018.2885794.
[9] H. Zhang, R. Zhang, and H. Huang: Some picture fuzzy dombi heronian mean operators with their application to multi-attribute decision-making. Symmetry, 10(11), (2018), 593, DOI: 10.3390/sym10110593.
[10] K.T. Atanassov: Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), (1986), 87–96, DOI: 10.1016/S0165-0114(86)80034-3.
[11] R.R.Yager: Pythagorean membership grades in multicriteria decision making. IEEE Transactions on Fuzzy Systems, 22(4), (2014), 958–965, DOI: 10.1109/TFUZZ.2013.2278989.
[12] J. Wang, R. Zhang, X. Zhu, Z. Zhou, X. Shang, and W. Li: Some q-rung orthopair fuzzy Muirhead means with their application to multi-attribute group decision making. Journal of Intelligent and Fuzzy Systems, 36(2), (2019), 1599–1614, DOI: 10.3233/JIFS-18607.
[13] R.R. Yager: Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), (2017), 1222–1230, DOI: 10.1109/TFUZZ.2016.2604005.
[14] P. Liu and P.Wang: Some q-rung orthopair fuzzy aggregation operators and their applications to multiple-attribute decision making. International Journal of Intelligent Systems, 33(4), (2017), 259–280, DOI: 10.1002/int.21927.
[15] C. Bonferroni: Sulle medie multiple di potenze. Bollettino dell’Unione Matematica Italiana, 5(3-4), (1950), 267–270. [16] S. Sykora: Mathematical means and averages: Generalized Heronian means. Stan’s Library, Ed. S. Sykora, 3, (2009), DOI: 10.3247/SL3Math 09.002.
[17] C. Maclaurin: A second letter to Martin Folkes, Esq.: concerning the roots of equations, with the demonstration of other rules in algebra. Phil, Transaction (1683–1775), 394, (1729), 59–96.
[18] R.F. Muirhead: Some methods applicable to identities and inequalities of symmetric algebraic functions of n letters. Proceedings of the Edinburgh Mathematical Societ., 21, (1902), 144–162, DOI: 10.1017/ S001309150003460X.
[19] P. Liu and J. Liu: Some q-rung orthopair fuzzy Bonferroni mean operators and their application to multi-attribute group decision making. International Journal of Intelligent Systems, 33(2), (2018), 315–347, DOI: 10.1002/int.21933.
[20] G. Wei, H. Gao, and Y. Wei: Some q-rung orthopair fuzzy Heronian mean operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(7), (2017), 1426–1458, DOI: 10.1002/int.21985.
[21] P. Liu and D. Li: Some Muirhead mean operators for intuitionistic fuzzy numbers and their applications to group decision making. PloS ONE, 12(1), (2017), 423–431, DOI: 10.1371/journal.pone.0168767.
[22] G. Wu, H. Garg, H. Gao, and C. Wei: Interval-valued Pythagorean fuzzy maclaurin symmetric mean operators in multiple attribute decision making. IEEE Access, 99(1), (2018), 67866–67884, DOI: 10.1109/ACCESS.2018.2877725.
[23] K. Bai, X. Zhu, J. Wang, and R. Zhang: Some partitioned Maclaurin symmetric mean based on q-rung orthopair fuzzy information for dealing with multi-attribute group decision making. Symmetry, 10(9), (2018), 383, DOI: 10.3390/sym10090383.
[24] G. Wei and M. Lu: Pythagorean fuzzy Maclaurin symmetric mean operators in multiple attribute decision making. International Journal of Intelligent Systems, 33(6), (2017), 1043–1070, DOI: 10.1002/int.21911.
[25] J. Qin: Generalized Pythagorean fuzzy Maclaurin symmetric means and its application to multiple attribute SIR group decision model. Journal of Intelligent and Fuzzy Systems, 20(1), (2017), 943–957, DOI: 10.1007/s40815- 017-0439-2.
[26] P. Liu, and X. Qin: Maclaurin symmetric mean operators of linguistic intuitionistic fuzzy numbers and their application to multiple-attribute decisionmaking. Journal of Experimental & Theoretical Artificial Intelligence, 29(6), (2017), 1–30, DOI: 10.1080/0952813X.2017.1310309.
[27] H. Wang, P. Liu, and Z. Liu: Trapezoidal interval type-2 fuzzy Maclaurin symmetric mean operators and their applications to multiple attribute group decision making. International Journal for Uncertainty Quantification, 8(44), (2018), 343–360, DOI: 10.1615/Int.J.UncertaintyQuantification.2018020768.
[28] H. Garg: Hesitant Pythagorean fuzzy Maclaurin symmetric mean operators and its applications to multiattribute decision-making process. International Journal of Intelligent Systems, 34(4), (2019), 601–626, DOI: 10.1002/int.22067.
[29] K.T. Atanassov and G. Gargov: Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31, (1989), 343–349, DOI: 10.1016/0165-0114(89)90205-4.
[30] H. Garg: A novel accuracy function under interval-valued Pythagorean fuzzy environment for solving multicriteria decision making problem. Journal of Intelligent and Fuzzy Systems, 31(1), (2016), 529–540, DOI: 10.3233/IFS-162165.
[31] B.P. Joshi, A. Singh, P.K. Bhatt, and K.S. Vaisla: Interval valued q-rung orthopair fuzzy sets and their properties. Journal of Intelligent and Fuzzy Systems, 35(5), (2018), 5225–5230, DOI: 10.3233/JIFS-169806.
[32] H. Kalani, M. Akbarzadeh, A. Akbarzadeh, and I. Kardan: Intervalvalued fuzzy derivatives and solution to interval-valued fuzzy differential equations. Journal of Intelligent and Fuzzy Systems, 30(6), (2016), 3373– 3384, DOI: 10.3233/IFS-162085.
[33] T. Chen: An interval-valued Pythagorean fuzzy outranking method with a closeness-based assignment model for multiple criteria decision making. International Journal of Intelligent Systems, 33(2), (2017), 126–168, DOI: 10.1002/int.21943.
[34] Z. Li, G. Wei, and H. Gao: Methods for multiple attribute decision making with interval-valued Pythagorean fuzzy information. Mathematics, 6, (2018), 228, DOI: 10.3390/math6110228.
[35] N. Jan, T. Mahmood, L. Zedam, K.Ullah, J.C. Alcantud, and B.Davvaz: Analysis of social networks, communication networks and shortest path problems in the environment of interval valued q-rung orthopair fuzzy information. Journal of Intelligent and Fuzzy Systems, 21, (2019), 1687– 1708, DOI: 10.1007/s40815-019-00643-9.
[36] H. Gao, Y. Ju, W. Zhang, and D. Ju: Multi-attribute decision-making method based on interval-valued q-rung orthopair fuzzy archimedean Muirhead mean operators. IEEE Access, 99(1), (2019), 74300–74315, DOI: 10.1109/ACCESS.2019.2918779.
[37] V. Torra: Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), (2010), 529–539, DOI: 10.1002/int.20418.
[38] B. Zhu, Z. Xu, and M. Xia: Dual hesitant fuzzy sets. Journal of Applied Mathematics, 2012, (2012), 1–13, DOI: 10.1155/2012/879629.
[39] D. Yu, W. Zhang, and G.Q. Huang: Dual hesitant fuzzy aggregation operators. textitTechnological and Economic Development of Economy, 22(2), (2015), 1–16, DOI: 10.3846/20294913.2015.1012657.
[40] Y. Xing, R. Zhang, M. Xia, and J. Wang: Generalized point aggregation operators for dual hesitant fuzzy information. Journal of Intelligent and Fuzzy Systems, 33(1), (2017), 515–527, DOI: 10.3233/JIFS-161922.
[41] Z. Su, Z. Xu, H. Zhao, and S. Liu: Distribution-based approaches to deriving weights from dual hesitant fuzzy information. Symmetry, 11(1), (2019), 85, DOI: 10.3390/sym11010085.
[42] G. Maity, D. Mardanya, S.K. Roy, and G.W. Weber: A new approach for solving dual-hesitant fuzzy transportation problem with restrictions, S¯adhan¯a, 44(75), (2019), DOI: 10.1007/s12046-018-1045-1.
[43] G. Qu, Q. An, W. Qu, F. Deng, and T. Li: Multiple attribute decision making based on bidirectional projection measures of dual hesitant fuzzy set. Journal of Intelligent and Fuzzy Systems, 7(5), (2019), 7087–7102, DOI: 10.3233/JIFS-181970.
[44] Y. Xu, X. Shang, J.Wang, H. Zhao, R. Zhang, and K. Bai: Some intervalvalued q-rung dual hesitant fuzzy Muirhead mean operators with their application to multi-attribute decision-making. IEEE Access, 99(1), (2019), 54724–54745, DOI: 10.1109/ACCESS.2019.2912814.
[45] T. Zhu, L. Luo, H. Liao, X. Zhang, and W. Shen: A hybrid multicriteria decision making model for elective admission control in a Chinese public hospital. Knowledge-Based Systems, 173, (2019), 37–51, DOI: 10.1016/j.knosys.2019.02.020.
[46] X. Gou, Z. Xu, H. Liao, and F. Herrera: Multiple criteria decision making based on distance and similarity measures under double hierarchy hesitant fuzzy linguistic environment. Computers & Industrial Engineering, 126, (2018), 516–530, DOI: 10.1016/j.cie.2018.10.020.
[47] Y. Xu, X. Shang, J. Wang, W. Wu, and H. Huang: Some q-rung dual hesitant fuzzy Heronian mean operators with their application to multiple attribute group decision-making. Symmetry, 10(10), (2018), 472, DOI: 10.3390/sym10100472.
[48] Y. Ju, X. Liu, and S. Yang: Interval-valued dual hesitant fuzzy aggregation operators and their applications to multiple attribute decision making. Journal of Intelligent and Fuzzy Systems, 27(3), (2014), 1203–1218, DOI: 10.3233/IFS-131085.
[49] W. Yang and Y. Pang: Hesitant interval-valued Pythagorean fuzzy VIKOR method. International Journal of Intelligent Systems, 34(5), (2018), 754– 789, DOI: 10.1002/int.22075.
[50] H. Hiidenhovi, P. Laippala, and K. Nojonen: Development of a patientorientated instrument to measure service quality in outpatient departments. Journal of Advanced Nursing, 34(5), (2001), 696–705, DOI: 10.1046/j.1365-2648.2001.01799.x.
[51] L. Li and W. Benton: Hospital capacity management decisions: Emphasis on cost control and quality enhancement. European Journal of Operational Research, 146(3), (2003), 596–614, DOI: 10.1016/S0377-2217(02)00225-4.
[52] C. Tian, Y. Tian, and L. Zhang: An evaluation scale of medical services quality based on “patients’ experience”. Journal of Huazhong University of Science and Technology [Medical Sciences], 34, (2014), 289–297, DOI: 10.1007/s11596-014-1273-5.
[53] S. Das, B. Dutta, and De. Guha: Weight computation of criteria in a decision-making problem by knowledge measure with intuitionistic fuzzy set and interval-valued intuitionistic fuzzy set. Soft Computing, 20(9), (2016), 3421–3442, DOI: 10.1007/s00500-015-1813-3.
[54] W. Zhang, X. Li, and Y. Ju: Some aggregation operators based on Einstein operations under interval-valued dual hesitant fuzzy setting and their application. Mathematical Problems in Engineering, 1, (2014), DOI: 10.1155/2014/958927.
[55] K. Rahman, S. Abdullah, M. Shakeel, M.S. Khan, and M. Ullah: Interval-valued Pythagorean fuzzy geometric aggregation operators and their application to group decision making problem. Cogent Mathematics, 4, (2017), DOI: 10.1080/23311835.2017.1338638.
[56] Y. Zang, X. Zhao, and S. Li: Interval-valued dual hesitant fuzzy Heronian mean aggregation operators and their application to multi-attribute decision making, International Journal of Computational Intelligence and Applications, 17(4), (2018), DOI: 10.1142/S1469026818500050.
[57] J. Wang, X. Shang, X. Feng, and M. Sun: A novel multiple attribute decision making method based on q-rung dual hesitant uncertain linguistic sets and Muirhead mean. Archives of Control Sciences, 30(2), (2020), 233– 272, DOI: 10.24425/acs.2020.133499.
[58] L. Li, R. Zhang, J. Wang, and X. Shang: Some q-orthopair linguistic Heronian mean operators with their application to multi-attribute group decision making. Archives of Control Sciences, 28(4), (2018), 551–583, DOI: 10.24425/acs.2018.125483.
[59] A. Biswas and A. Sarkar: Development of dual hesitant fuzzy prioritized operators based on Einstein operations with their application to multicriteria group decision making. Archives of Control Sciences, 28(4), (2018), 527–549, DOI: 10.24425/acs.2018.125482.
Przejdź do artykułu

Autorzy i Afiliacje

Butian Zhao
1
Runtong Zhang
1
Yuping Xing
2

  1. School of Management and Economic, Beijing Jiaotong University, Beijing, 100044, China
  2. Glorious Sun School of Business and Management, DongHua University, Shanghai, 200051, China
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

In this paper, we introduce necessary and sufficient efficiency conditions associated with a class of multiobjective fractional variational control problems governed by geodesic quasiinvex multiple integral functionals and mixed constraints containing m-flow type PDEs. Using the new notion of ( normal) geodesic efficient solution, under ( p; b)-geodesic quasiinvexity assumptions, we establish sufficient efficiency conditions for a feasible solution.
Przejdź do artykułu

Bibliografia

[1] R.P. Agarwal, I. Ahmad, A. Iqbal, and S. Ali: Generalized invex sets and preinvex functions on Riemannian manifolds, Taiwanese J. Math., 16(5), (2012), 1719–1732, DOI: 10.11650/twjm/1500406792.
[2] T. Antczak: G-pre-invex functions in mathematical programming, J. Comput. Appl. Math., 217(1), (2008), 212–226, DOI: 10.1016/j.cam.2007.06.026.
[3] M. Arana-Jimenez, B. Hernandez-Jimenez, G. Ruiz-Garzon, and A. Rufian-Lizana: FJ-invex control problem, Appl. Math. Lett., 22(12), (2009), 1887–1891, DOI: 10.1016/j.aml.2009.07.016.
[4] A. Barani and M.R. Pouryayevali: Invex sets and preinvex functions on Riemannian manifolds, J. Math. Anal. Appl., 328(2), (2007), 767–779, DOI: 10.1016/j.jmaa.2006.05.081.
[5] M.A. Hanson: On sufficiency of Kuhn-Tucker conditions, J. Math. Anal. Appl., 80(2), (1981), 545–550, DOI: 10.1016/0022-247X(81)90123-2.
[6] R. Jagannathan: Duality for nonlinear fractional programs, Z. Oper. Res., 17(1-3), (1973), DOI: 10.1007/BF01951364.
[7] V. Jeyakumar: Strong and weak invexity in mathematical programming, Research report (University of Melbourne, Department of Mathematics), 1984, no. 29.
[8] D.H. Martin: The essence of invexity, J. Optim. Theory Appl., 47(1), (1985), 65–76, DOI: 10.1007/BF00941316.
[9] St. Mititelu: Optimality and duality for invex multi-time control problems with mixed constraints, J. Adv. Math. Stud., 2(1), (2009), 25–34.
[10] St. Mititelu, M.Constantinescu, and C. Udriste: Efficiency for multitime variational problems with geodesic quasiinvex functionals on Riemannian manifolds, BSG Proceedings 22. The Intern. Conf. “Differential Geometry – Dynamical Systems”, September 1-4, 2014, Mangalia-Romania, pp. 38–50. Balkan Society of Geometers, Geometry Balkan Press 2015.
[11] St. Mititelu and S. Treanta: Efficiency conditions in vector control problems governed by multiple integrals, J. Appl. Math. Comput., 57(1-2), (2018), 647–665, DOI: 10.1007/s12190-017-1126-z.
[12] M.A. Noor and K.I. Noor: Some characterizations of strongly preinvex functions, J. Math. Anal. Appl., 316(2), (2006), 697–706, DOI: 10.1016/ j.jmaa.2005.05.014.
[13] V.A. de Oliveira and G.N. Silva: On sufficient optimality conditions for multiobjective control problems, J. Global Optim., 64(4), (2016), 721–744, DOI: 10.1007/s10898-015-0351-y.
[14] R. Pini: Convexity along curves and indunvexity, Optimization, 29(4), (1994), 301–309, DOI: 10.1080/02331939408843959.
[15] T. Rapcsak: Smooth Nonlinear Optimization in Rn, Nonconvex Optimization and Its Applications, Kluwer Academic, 1997.
[16] W. Tang and X. Yang: The sufficiency and necessity conditions of strongly preinvex functions, OR Transactions, 10, 3, (2006), 50–58. [17] S. Treanta: PDEs of Hamilton-Pfaff type via multi-time optimization problems, U.P.B. Sci. Bull., Series A: Appl. Math. Phys., 76(1), (2014), 163–168.
[18] S. Treanta: Optimal control problems on higher order jet bundles. The Intern. Conf. “Differential Geometry – Dynamical Systems”, October 10- 13, 2013, Bucharest-Romania, pp. 181–192. Balkan Society of Geometers, Geometry Balkan Press 2014.
[19] S. Treanta: Multiobjective fractional variational problem on higherorder jet bundles, Commun. Math. Stat., 4(3), (2016), 323–340, DOI: 10.1007/s40304-016-0087-0.
[20] S. Treanta: Higher-order Hamilton dynamics and Hamilton-Jacobi divergence PDE, Comput. Math. Appl., 75(2), (2018), 547–560, DOI: 10.1016/j.camwa.2017.09.033.
[21] S. Treanta and M. Arana-Jimenez: KT-pseudoinvex multidimensional control problem, Optim. Control Appl. Meth., 39(4), (2018), 1291–1300, DOI: 10.1002/oca.2410.
[22] S. Treanta and M. Arana-Jimenez: On generalized KT-pseudoinvex control problems involving multiple integral functionals, Eur. J. Control, 43, (2018), 39–45, DOI: 10.1016/j.ejcon.2018.05.004.
[23] S. Treanta: Efficiency in generalized V-KT-pseudoinvex control problems, Int. J. Control, 93(3), (2020), 611–618, DOI: 10.1080/00207179.2018.1483082.
[24] C. Udriste: Convex Functions and Optimization Methods on Riemannian Manifolds, Mathematics and Its Applications, KluwerAcademic, 297, 1994.
[25] T. Weir and B. Mond: Pre-invex functions in multiple objective optimization, J. Math. Anal. Appl., 136(1), (1988), 29–38, DOI: 10.1016/0022-247X(88)90113-8.
Przejdź do artykułu

Autorzy i Afiliacje

Savin Treanţă
1
Ştefan Mititelu
2

  1. University “Politehnica”of Bucharest, Faculty of Applied Sciences, Department of Applied Mathematics, 313 Splaiul Independentei, 060042 – Bucharest, Romania
  2. Technical University of Civil Engineering, Department of Mathematics and Informatics, 124 Lacul Tei, 020396 – Bucharest, Romania
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

This paper presents a new grid integration control scheme that employs spider monkey optimization technique for maximum power point tracking and Lattice Levenberg Marquardt Recursive estimation with a hysteresis current controller for controlling voltage source inverter. This control scheme is applied to a PV system integrated to a three phase grid to achieve effective grid synchronization. To verify the efficacy of the proposed control scheme, simulations were performed. From the simulation results it is observed that the proposed controller provides excellent control performance such as reducing THD of the grid current to 1.75%.
Przejdź do artykułu

Bibliografia

[1] I. Dincer: Renewable energy and sustainable development: a crucial review. Renewable and Sustainable Energy Reviews, 4(2), (2000), 157–175, DOI: 10.1016/S1364-0321(99)00011-8.
[2] S. Gulkowski, J.V.M. Diez, J.A. Tejero, and G. Nofuentes: Computational modeling and experimental analysis of heterojunction with intrinsic thin-layer photovoltaic module under different environmental conditions. Energy, 172, (2019), 380–390, DOI: 10.1016/j.energy.2019.01.107.
[3] M. Bahrami, et al.: Hybrid maximum power point tracking algorithm with improved dynamic performance. Renewable Energy, 130, (2019), 982–991, DOI: 10.1016/j.renene.2018.07.020.
[4] K.V. Singh, Krishna, H. Bansal, and D. Singh: A comprehensive review on hybrid electric vehicles: architectures and components. Journal of Modern Transportation, 27, (2019), 1–31, DOI: 10.1007/s40534-019-0184-3.
[5] S. Pradhan, et al.: Performance Improvement of Grid-Integrated Solar PV System Using DNLMS Control Algorithm. IEEE Transactions on Industry Applications, 55(1), (2019), 78–91, DOI: 10.1109/TIA.2018.2863652.
[6] S. Negari and D. Xu: Utilizing a Lagrangian approach to compute maximum fault current in hybrid AC–DC distribution grids withMMCinterface. High Voltage, 4(1), (2019), 18–27, DOI: 10.1049/hve.2018.5087.
[7] V.T. Tran et al.: Mitigation of Solar PV Intermittency Using Ramp-Rate Control of Energy Buffer Unit. IEEE Transactions on Energy Conversion, 34(1), (2019), 435–445, DOI: 10.1109/TEC.2018.2875701.
[8] A. Kihal, et al.: An improved MPPT scheme employing adaptive integral derivative sliding mode control for photovoltaic systems under fast irradiation changes. ISA Transactions, 87, (2019), 297–306, DOI: 10.1016/j.isatra.2018.11.020.
[9] A.M. Jadhav, N.R. Patne, and J.M. Guerrero: A novel approach to neighborhood fair energy trading in a distribution network of multiple microgrid clusters. IEEE Transactions on Industrial Electronics, 66(2), (2019), 1520– 1531, DOI: 10.1109/TIE.2018.2815945.
[10] A. Fragaki, T. Markvart, and G. Laskos: All UK electricity supplied by wind and photovoltaics – The 30–30 rule. Energy, 169, (2019), 228–237, DOI: 10.1016/j.energy.2018.11.151.
[11] S.Z. Ahmed, et al.: Power quality enhancement by using D-FACTS systems applied to distributed generation. International Journal of Power Electronics and Drive Systems, 10(1), (2019), 330, DOI: 10.11591/ijpeds.v10.i1.pp330-341.
[12] H.H. Alhelou, et al.: A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges. Energies. 12(4), (2019), 1– 28, DOI: 10.3390/en12040682.
[13] M. Badoni, A. Singh, and B. Singh: Implementation of Immune Feedback Control Algorithm for Distribution Static Compensator. IEEE Transactions on Industry Applications, 55(1), (2019), 918–927, DOI: 10.1109/TIA.2018.2867328.
[14] S.R. Das, et al.: Performance evaluation of multilevel inverter based hybrid active filter using soft computing techniques. Evolutionary Intelligence (2019), 1–11, DOI: 10.1007/s12065-019-00217-6.
[15] F. Chishti, S. Murshid, and B. Singh: LMMN Based Adaptive Control for Power Quality Improvement of Grid Intertie Wind-PV System. IEEE Transactions on Industrial Informatics, 15(9), (2019), 4900–4912, DOI: 10.1109/TII.2019.2897165.
[16] S. Pradhan, et al.: Performance Improvement of Grid-Integrated Solar PV System Using DNLMS Control Algorithm. IEEE Transactions on Industry Applications, 55(1), (2019), 78–91, DOI: 10.1109/IICPE.2016.8079455.
[17] V. Jain, I. Hussain, and B. Singh: A HTF-Based Higher-Order Adaptive Control of Single-Stage Grid-Interfaced PV System. IEEE Transactions on Industry Applications, 55(2), (2019), 1873–1881, DOI: 10.1109/TIA.2018.2878186.
[18] N. Kumar, B. Singh, B. Ketan Panigrahi and L. Xu: Leaky Least Logarithmic Absolute Difference Based Control Algorithm and Learning Based InC MPPT Technique for Grid Integrated PV System. IEEE Transactions on Industrial Electronics. 66(11), (2019), 9003–9012, DOI: 10.1109/TIE.2018.2890497.
[19] P. Shah, I. Hussain, and B. Singh: Single-Stage SECS Interfaced with Grid Using ISOGI-FLL- Based Control Algorithm. IEEE Transactions on Industry Applications, 55(1), (2019), 701–711, DOI: 10.1109/TIA.2018.2869880.
[20] V. Jain and B. Singh: A Multiple Improved Notch Filter-Based Control for a Single-StagePVSystem Tied to aWeak Grid. IEEE Transactions on Sustainable Energy, 10(1), (2019), 238–247, DOI: 10.1109/TSTE.2018.2831704.
[21] N. Mohan and T. M. Undeland: Power electronics: converters, applications, and design. John Wiley & Sons, 2007.
[22] M. Badoni, et al.: Grid interfaced solar photovoltaic system using ZA-LMS based control algorithm. Electric Power Systems Research, 160, (2018), 261–272, DOI: 10.1016/j.epsr.2018.03.001.
[23] M. Rezkallah, et al.: Lyapunov function and sliding mode control approach for the solar-PV grid interface system. IEEE Transactions on Industrial Electronics, 64(1), (2016), 785–795, DOI: 10.1109/tie.2016.2607162.
[24] N. Kumar, B. Singh, and B.K. Panigrahi: Integration of Solar PV with Low- Voltage Weak Grid System: using Maximize-M Kalman Filter and Self-Tuned P&O Algorithm. IEEE Transactions on Industrial Electronics, 66(11), (2019), 9013–9022, DOI: 10.1109/tie.2018.2889617.
[25] H. Sharma, G. Hazrati, and J.Ch.Bansal: Spider monkey optimization algorithm. Evolutionary and swarm intelligence algorithms. Springer, Cham, 2019, 43–59.
[26] K. Neelu, P. Devan, Ch.L. Chowdhary, S. Bhattacharya, G. Singh, S. Singh, and B. Yoon: Smo-dnn: Spider monkey optimization and deep neural network hybrid classifier model for intrusion detection. Electronics, 9(4), (2020), 692, DOI: 10.3390/electronics9040692.
[27] M.A.H. Akhand, S.I. Ayon, A.A. Shahriyar, and N. Siddique: Discrete spider monkey optimization for travelling salesman problem. Applied Soft Computing, 86 (2020), DOI: 10.1016/j.asoc.2019.105887.
[28] Avinash Sharma, Akshay Sharma, B.K. Panigrahi, D. Kiran, and R. Kumar: Ageist spider monkey optimization algorithm. Swarm and Evolutionary Computation, 28 (2016), 58–77, DOI: 10.1016/j.swevo.2016.01.002.
[29] Sriram Mounika and K. Ravindra: Backtracking Search Optimization Algorithm Based MPPT Technique for Solar PV System. In Advances in Decision Sciences, Image Processing, Security and Computer Vision. Springer, Cham, 2020, 498–506.
[30] Pilakkat, Deepthi and S. Kanthalakshmi: Single phase PV system operating under Partially Shaded Conditions with ABC-PO as MPPT algorithm for grid connected applications. Energy Reports, 6 (2020), 1910–1921, DOI: 10.1016/j.egyr.2020.07.019.
[31] R. Gessing: Controllers of the boost DC-DC converter accounting its minimum- and non-minimum-phase nature. Archives of Control Sciences, 19(3), (2009), 245–259.
[32] A. Talha and H. Boumaaraf: Evaluation of maximum power point tracking methods for photovoltaic systems. Archives of Control Sciences, 21(2), (2011), 151–165.
[33] S.N. Singh and S. Mishra: FPGA implementation of DPWM utility/DG interfaced solar (PV) power converter for green home power supply. Archives of Control Sciences, 21(4), (2011), 461–469.
Przejdź do artykułu

Autorzy i Afiliacje

Dipak Kumar Dash
1
Pradip Kumar Sadhu
1
Bidyadhar Subudhi
2

  1. Department of Electrical Engineering, Indian Institute of Technology (ISM), Dhanbad, India
  2. School of Electrical Sciences, Indian Institute of Technology Goa, GEC Campus, Farmagudi, Ponda-401403, Goa, India
Pobierz PDF Pobierz RIS Pobierz Bibtex

Abstrakt

The purpose of this paper is to introduce a new chaotic oscillator. Although different chaotic systems have been formulated by earlier researchers, only a few chaotic systems exhibit chaotic behaviour. In this work, a new chaotic system with chaotic attractor is introduced. It is worth noting that this striking phenomenon rarely occurs in respect of chaotic systems. The system proposed in this paper has been realized with numerical simulation. The results emanating from the numerical simulation indicate the feasibility of the proposed chaotic system. More over, chaos control, stability, diffusion and synchronization of such a system have been dealt with.
Przejdź do artykułu

Bibliografia

[1] M.P. Kennedy: Chaos in the Colpitts oscillator. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 41 (1994), 771–774, DOI: 10.1109/81.331536.
[2] S. Vaidyanathan, K. Rajagopal, C.K. Volos, I.M. Kyprianidis, and I.N. Stouboulos: Analysis, adaptive control and synchronization of a seventerm novel 3-D chaotic system with three quadratic nonlinearities and its digital implementation in labview. Journal of Engineering Science and Technology Review, 8 (2015), 130–141.
[3] P. Kvarda: Identifying the deterministic chaos by using the Lyapunov exponents. Radioengineering-Prague, 10 (2001), 38–38.
[4] Y.C. Lai and C. Grebogi: Modeling of coupled chaotic oscillators. Physical Review Letters, 82 (1999), 4803, DOI: 10.1103/PhysRevLett.82.4803.
[5] H. Deng and D. Wang: Circuit simulation and physical implementation for a memristor-based Colpitts oscillator. AIP Advances, 7 (2017), 035118, DOI: 10.1063/1.4979175.
[6] A. Cenys, A. Tamasevicius, A.Baziliauskas, R. Krivickas, and E. Lind- berg: Hyperchaos in coupled Colpitts oscillators. Chaos, Solitons & Fractals, 17 (2003), DOI: 10.1016/S0960-0779(02)00373-9.
[7] C.M. Kim, S. Rim, W.H. Kye, J.W. Ryu, and Y.J. Park: Anti-synchronization of chaotic oscillators. Physics Letters A, 320 (2003), 39–46, DOI: 10.1016/j.physleta.2003.10.051.
[8] A.S. Elwakil and M.P. Kennedy: A family of Colpitts-like chaotic oscillators. Journal of the Franklin Institute, 336 (1999), 687–700, DOI: 10.1016/S0016-0032(98)00046-5.
[9] S. Vaidyanathan, A. Sambas, and S. Zhang: A new 4-D dynamical system exhibiting chaos with a line of rest points, its synchronization and circuit model. Archives of Control Sciences, 29 (2019), DOI: 10.24425/acs.2019.130202.
[10] C.K. Volos, V.T. Pham, S. Vaidyanathan, I.M. Kyprianidis, and I.N. Stouboulos: Synchronization phenomena in coupled Colpitts circuits. Journal of Engineering Science & Technology Review, 8 (2015).
[11] H. Fujisaka and T. Yamada: Stability theory of synchronized motion in coupled-oscillator systems. Progress of theoretical physics, 69 (1983), 32– 47, DOI: 10.1143/PTP.69.32.
[12] N.J. Corron, S.D. Pethel, and B.A. Hopper: Controlling chaos with simple limiters. Physical Review Letters, 84 (2000), 3835, DOI: 10.1103/Phys-RevLett.84.3835.
[13] J.Y. Effa, B.Z. Essimbi, and J.M. Ngundam: Synchronization of improved chaotic Colpitts oscillators using nonlinear feedback control. Nonlinear Dynamics, 58 (2009), 39–47, DOI: 10.1007/s11071-008-9459-7.
[14] S. Mishra, A.K. Singh, and R.D.S. Yadava: Effects of nonlinear capacitance in feedback LC-tank on chaotic Colpitts oscillator. Physica Scripta, 95 (2020), 055203. DOI: 10.1088/1402-4896/ab6f95.
[15] S. Vaidyanathan and S. Rasappan: Global chaos synchronization of nscroll Chua circuit and Lur’e system using backstepping control design with recursive feedback. Arabian Journal for Science and Engineering, 39 (2014), 3351–3364, DOI: 10.1007/s13369-013-0929-y.
[16] R. Suresh and V. Sundarapandian: Hybrid synchronization of nscroll Chua and Lur’e chaotic systems via backstepping control with novel feedback. Archives of Control Sciences, 22 (2012), 343–365, DOI: 10.2478/v10170-011-0028-9.
[17] S. Rasappan: Synchronization of neuronal bursting using backstepping control with recursive feedback. Archives of Control Sciences, 29 (2019), 617–642, DOI: 10.24425/acs.2019.131229.
[18] H.B. Fotsin and J.Daafouz:Adaptive synchronization of uncertain chaotic Colpitts oscillators based on parameter identification. Physics Letters A, 339 (2005), 304–315, DOI: 10.1016/j.physleta.2005.03.049.
[19] S. Sarkar, S. Sarkar, and B.C. Sarkar: On the dynamics of a periodic Colpitts oscillator forced by periodic and chaotic signals. Communications in Nonlinear Science and Numerical Simulation, 19 (2014), 2883–2896, DOI: 10.1016/j.cnsns.2014.01.004.
[20] S.T. Kammogne and H.B. Fotsin: Synchronization of modified Colpitts oscillators with structural perturbations. Physica scripta, 83 (2011), 065011, DOI: 10.1088/0031-8949/83/06/065011.
[21] S.T. Kammogne and H.B. Fotsin: Adaptive control for modified projective synchronization-based approach for estimating all parameters of a class of uncertain systems: case of modified Colpitts oscillators. Journal of Chaos, (2014), DOI: 10.1155/2014/659647.
[22] L.M. Pecora and T.L. Carroll: Synchronization in chaotic systems. Physical review letters, 64 (1990), 821, DOI: 10.1103/PhysRevLett.64.821.
[23] I. Ahmad and B. Srisuchinwong: A simple two-transistor 4D chaotic oscillator and its synchronization via active control. IEEE 26th International Symposium on Industrial Electronics, (2017) 1249–1254, DOI: 10.1109/ISIE.2017.8001424.
[24] S. Bumelien˙e, A. Tamasevicius, G. Mykolaitis, A. Baziliauskas, and E. Lindber: Numerical investigation and experimental demonstration of chaos from two-stage Colpitts oscillator in the ultrahigh frequency range. Nonlinear Dynamics, 44 (2006), 167–172, DOI: 10.1007/s11071-006-1962-0.
[25] F.Q. Wu, J. Ma, and G.D. Ren: Synchronization stability between initialdependent oscillators with periodical and chaotic oscillation. Journal of Zhejiang University-Science A, 19 (2018), 889–903, DOI: 10.1631/jzus.a1800334.
[26] G.H. Li, S.P. Zhou, and K. Yang: Controlling chaos in Colpitts oscillator. Chaos, Solitons & Fractals, 33 (2007), 582–587, DOI: 10.1016/j.chaos.2006.01.072.
[27] S. Vaidyanathan, S.A.J.A.D. Jafari, V.T. Pham, A.T. Azar, and F.E. Al- saadi: A 4-D chaotic hyperjerk system with a hidden attractor, adaptive backstepping control and circuit design. Archives of Control Sciences, 28 (2018), 239–254, DOI: 10.24425/123458.
[28] J.H. Park: Adaptive control for modified projective synchronization of a four-dimensional chaotic system with uncertain parameters. Journal of Computational and Applied Mathematics, 213 (2008), 288–293. DOI: 10.1016/j.cam.2006.12.003.
[29] M. Rehan: Synchronization and anti-synchronization of chaotic oscillators under input saturation. Applied Mathematical Modelling, 37 (2013), 6829– 6837. DOI: 10.1016/j.apm.2013.02.023.
[30] M.C. Liao, G. Chen, J.Y. Sze, and C.C. Sung: Adaptive control for promoting synchronization design of chaotic Colpitts oscillators. Journal of the Chinese Institute of Engineers, 31 (2008), 703–707. DOI: 10.1080/02533839.2008.9671423.
[31] S. Rasappan and S. Vaidyanathan: Hybrid synchronization of n-scroll chaotic Chua circuits using adaptive backstepping control design with recursive feedback. Malaysian Journal of Mathematical Sciences, 7 (2013), 219–246. DOI: 10.1080/23311916.2015.1009273.
[32] J. Kengne, J.C. Chedjou, G. Kenne, and K. Kyamakya: Dynamical properties and chaos synchronization of improved Colpitts oscillators. Communications in Nonlinear Science and Numerical Simulation, 17 (2012), 2914–2923. DOI: 10.1016/j.cnsns.2011.10.038.
[33] W. Hahn: Stability of motion. Springer, 138, 1967.
[34] J.P. Singh and B.K. Roy: The nature of Lyapunov exponents is (+,+,-,-). Is it a hyperchaotic system? Chaos, Solitons & Fractals, 92 (2016), 73–85. DOI: 10.1016/j.chaos.2016.09.010.
[35] A. Wolf, J.B. Swift, H.L. Swinney, and J.A. Vastano: Determining Lyapunov exponents from a time series. Physica D: Nonlinear Phenomena, 16 (1985), 285–317. DOI: 10.1016/0167-2789(85)90011-9.
Przejdź do artykułu

Autorzy i Afiliacje

Suresh Rasappan
1
K.A. Niranjan Kumar
1

  1. Department of Mathematics, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi, Chennai-62, India

Instrukcja dla autorów

Each paper submitted is subject to a review procedure, and the publication decision is based on reviers' comments on the paper. To avoid delay, please prepare the manuscript carefully following the suggestions listed below.

Computer file of the manuscript may be sent by e-mail to the address of Assistant Editor or acs@polsl.pl. Preferred text processors is TeX or LaTeX, however Word and other processors are also acceptable. In case of difficulties in processing the text, the author may be asked to supply the ASCII export of the original file.

Manuscripts sent via ordinary post should be typewritten double-spaced on one side of a standard size (A4) paper. Left side margin should be approximately 3cm (1.2'') wide. Each page should contain approximately 30 lines of 60 characters each. The manuscript including figures and tables together with their captions should be submitted. A separate signed letter giving the Author's preferred address for correspondence and return of proofs should be enclosed. Manuscript is the basis for editorial work.

First page should include the title of the paper, first name(s) and surname(s) of the Author(s), and a short summary (abstract), not longer than 20 lines.

Keywords of max. 5 - 7 items should be included in manuscript.

Numeration. All chapters, including the introduction, should be numbered in arabic numerals. Equations, tables and figures as well as theorems, corollaries, examples etc., should be numbered consecutively throughout the paper in arabic numerals, except in appendices. Appendices should be numbered with capital letters, and numeration should be closed within individual appendices.

If the manuscript is not prepared with TeX, mathematical expressions should be carefully written so as not to arouse confusion. Care should be taken that subscripts and superscripts are easily readable.

Tables and figures should be placed as desired by the Author within the text or on separate sheets with their suggested location indicated by the number of table or figure in the text. Figures, graphs and pictures (referred to as Fig. in the manuscript) should be numbered at the beginning of their caption following the figure. All figures should be prepared as PostScript EPS files or LaTeX picture files; in special cases, bitmaps of figure are also acceptable. The numbers and titles of tables should be placed above the main body of each table.

References should be listed alphabetically at the end of the manuscript. They should be numbered in ascending order and the numbers should be inserted in square brackets. References should be organized as follows. First initial(s), surname(s) of the author(s) and title of article or book. Then, for papers: title of periodical or collective work, volume number (year of issue), issue number, and numbers of the first and the last page; for books: publisher's name(s), place and year of issue. Example:

  1. R. E. Kalman: Mathematical description of linear dynamical system. SIAM J. Control. 1(2), (1963), 152-192.
  2. F. C. Shweppe: Uncertain dynamic systems. Prentice-Hall, Englewood Cliffs, N.J. 1970.


Please, give full titles of journals; only common words like Journal, Proceedings, Conference, etc. may be abbreviated ( to J., Proc., Conf., ... respectively). References to publications in the body of the manuscript should be indicated by the numbers of the adequate references in square brackets. When the paper is set in TeX the preferable form of preparing references is Bib TeX bib database.

Footnotes should be placed in the manuscript, beginning with "Received..." (date to be filled in by Editor), the author's institutional affiliation and acknowledgement of financial support,

Ta strona wykorzystuje pliki 'cookies'. Więcej informacji