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Tytuł artykułu

Laboratory bench to analyze of automatic control system with a fuzzy controller

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper represents laboratory bench to analyse a system of automated control with a fuzzy controller. The laboratory bench consists of a thermal object, and software and hardware complex involving logic controller VIPA System 200 V as well as HMI / SCADA system Zenon Supervisor 7.0. The thermal object is described with the help of the second-order differential equation using “current value within the power converter of electric heater-air temperature inside a thermal object” control channel. Coefficients of the differential equation depend upon location of a dampener and upon rotation frequency of a centrifugal fan. Control error (ie deviation between the specified temperature value within the thermal object and its current value), and derivative of the error, represented in the form of linguistic variables involving five triangular terms and two trapezoidal (extreme) ones have been used as the input values of the fuzzy controller. Output value of the fuzzy controller is the electric power supplied to the electric heater and assuming seven specified values. Selection of the specific value of electric power depends upon knowledge base being a finite set of rules of fuzzy sets falling into line with the applied linguistic variables. To implement such a system of automated control with a fuzzy controller, original software has been developed making it possible to analyze a process of thermal object heating with the use of human-computer interface. Interaction algorithm of certain program elements has been described. Experimental results, concerning the thermal object transfer from different initial conditions to terminal ones, have been demonstrated. A dependence of mean-square error of the controlled value upon the control period has been demonstrated.
Czasopismo
Rocznik
Strony
61--68
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
  • Department of Automation and Instrumentation, Dnipro University of Technology, D. Yavornitsky 19, Dnipro, Ukraine, 49027
  • Department of the Electrical Engineering and Electromechanic, National Metallurgical Academy of Ukraine, Gagarina ave., 4, Dnipro, Ukraine, 49600
  • Electric Power Department, Railway Research Institute, 50, Chlopickiego str., Warsaw, Poland
  • Department of Humanitarian, Fundamental and General Engineering Disciplines Institute of Integrated Education National metallurgical academy of Ukraine, Gagarina ave., 4, Dnipro, Ukraine, 49600
  • Department of Calculating Mathematics and Mathematical Cybernetics, Oles Honchar Dnipro National University, D. Yavornitsky ave., 35, Dnipro, Ukraine, 49600
  • Department of Calculating Mathematics and Mathematical Cybernetics, Oles Honchar Dnipro National University, D. Yavornitsky ave., 35, Dnipro, Ukraine, 49600
Bibliografia
  • 1. Chakraborty J, Jayanthi T, Satya Murty, SAV, Thirugnanamurthy, D, Swaminathan, P. Fuzzy logic based feed water flow control model for prototype fast breeder reactor. 2011 3rd IEEE International Conference on Computer Modeling and Simulation (ICCMS 2011): Conference Paper. Mumbai, 2011.
  • 2. Dubovoi VM, Kvietnyi RN, Mykhaliov OI, Usov AV. Modeling and optimization of systems: Textbook. Vinnytsia: TD “Edelweiss” Ltd.; 2017.
  • 3. Kovrygo YM, Novikov PV. Two-channel fuzzy controller to regulate technological parameters under conditions of nonstationary of dynamic characteristics of the controllable object. Automation of technological and business-processes 2019; 11(1): 4-13. Odesa.
  • 4. German EY, Shutynskyi OG, Lysachenko ІG, Demenkova SD. Optimal fuzzy controller in the system of carbonization control in sodium carbonate production. Modern information systems. Quarterly scientific and technical journal 2019; 3(2): 14-21. Kharkiv: NTU “KhPI”. https://doi.org/10.20998/2522-9052.2019.2.03.
  • 5. Solanke DR, Chinchkhede KD, Manwar AB. Design & Implementation of Fuzzy Inference System For Automatic Braking System. International journal of Reseach in Science and Engineering 2017; 6(9): 1242-1255.
  • 6. Vichuzhanin V. Realization of a fuzzy controller with fuzzy dynamic correction. Central European Journal of Engineering 2012; 2(3): 392-398. https://doi.org/10.2478/s13531-012-0003-7.
  • 7. Xie X, Long Z. Fuzzy PID Temperature control system design based on single chip microcomputer. Internetional Journal of Online and Biometrical Engineering 2015 11: 29-33. https://doi.org/10.3991/ijoe.v11i8.4881.
  • 8. Singhala P, Shah DN, Patel B. Temperature Control using Fuzzy Logic. International Journal of Instrumentation and Control Systems 2014; Vol.4. # 1: 1-10. https://doi.org/10.5121 / ijics.2014.4101.
  • 9. Atar KD, Hanamane MD, Patil AR, Mudholkar RR. Embedded fuzzy greenhouse parameter control and central monitoring system. International Journal of Engineering and Management Research 2014; 4( 4): 222-228.
  • 10. Pritchenko OV. The concept of the incentives of small-sized laboratory stands. Electromechanical and saving up systems. Quarterly scientific production journal. 2010; 2(10): 56-61. Kremenchuk: KSU.
  • 11. Adamczak S, Domagalski R, Sender E, Zmarzły P, Gorycki Ł. Research methods and testing stand developed to examine vibrations generated by rolling bearing. Diagnostyka 2016; 17 (1): 41-49.
  • 12. Roczek K, Rogala T. Induction motor diagnosis with use of electric parameters. Diagnostyka 2019; 20 (4): 65-74. https://doi.org/10.29354/diag/113000.
  • 13. Tryputen N, Kuznetsov V, Kuznetsova Y. About the possibility of researching the optimal automatic control system on a physical model of a thermal object. 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2019 - Proceedings: 1244-1248. L'viv, Ukraine https://doi.org/10.1109 / UKRCON.2019.8879830.
  • 14. Mykola T, Vitaliy K, Serdiuk T, Alisa K, Maksym T, Mykola B. One approach to quasi-optimal control of direct current motor. 2019 IEEE 5th International Conference Actual Problems of Unmanned Aerial Vehicles Developments, APUAVD 2019-Proceedings: 190-193. https://doi.org/10.1109/APUAVD47061.2019.8943878.
  • 15. Kuzenkov O, Kuznetsov V, Tryputen N. Analysis of phase trajectories of the third - Order dynamic objects. 2019 IEEE 2nd Ukraine Conference on Electrical and Computer Engineering, UKRCON 2019 - Proceedings: 1235-1243. L'viv, Ukraine https://doi.org/10.1109/UKRCON.2019.8879819.
  • 16. Perekrest A, Chornyi O, Mur O, Nikolenko A, Kuznetsov V, Kuznetsova Y. Preparation and preliminary analysis of data on energy consumption by municipal buildings. Eastern-European Journal of Enterprise Technologies 2018;6(8-96);32-42. https://doi.org/10.15587/1729-4061.2018.147485.
  • 17. Pashchenko FF, Kudinov YI, Pashchenko AF, Duvanov ES. Fuzzy quadratic control of thermal object. 2019 1st International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA): 2019:288-293. https://doi.org/10.1109/SUMMA48161.2019.8947607.
  • 18. Kudinov YI, Kelina AY, Pashchenko AF, Pashchenko FF. Optimization of intelligent fuzzy controllers for industrial facilities. 2014 IEEE 8th International Conference on Application of Information and Communication Technologies (AICT):, 2014: 1-3. https://doi.org/10.1109/ICAICT.2014.7035988.
  • 19. Dong Z, Su Y, Yan X. Temperature Control system of the thermal analyzer based on fuzzy PID controller. 2009 Ninth International Conference on Hybrid Intelligent Systems: 2009:58-61. Shenyang,. https://doi.org/10.1109 / HIS.2009.123.
  • 20. Shishov OV. Elements of automation systems. Controllers, operator panels, remote access modules: laboratory practice. Direkt-Media, Moscow, Russia - Berlin, Germany; 2015.
  • 21. Manual VIPA System 200V. CP Manual HB97E_CP RE_240-1CA21.
  • 22. Banerjee A, Mondal A, Sarkar A, Biswas S. Realtime embedded systems analysis - From theory to practice. 2015 19th International Symposium on VLSI Design and Test. Ahmedabad, 2015, pp. 1-2. https://doi.org/10.1109 / ISVDAT.2015.7208162.
  • 23. Buttazzo GC. Hard Real-time computing systems: predictable scheduling algorithms and applications. Springer Science + Business Media, LLC, New York, 2011:536.https://doi.org/10.1007/978-1-4614-0676-1.
  • 24. Mall Rajib. Real-time systems: theory and practice. Publisher: Prentice Hall; 2009: 242.
  • 25. ;Dwyer A. Handbook of PI and PID controller tuning rules. " 3rd ed. Dublin Institute of Technology; 2009: 624.
  • 26. Rajendran M, Kumar G. Comparative Analysis of PI / PID Controller for a Thermal Process Using PLC. b2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE): 2839-2841. Bhubaneswar. 2018. https://doi.org/10.1109/ICRIEECE44171.2018.9008671.
  • 27. Gozim D, Guesmi K, Mahi D. On the elimination of nonlinear phenomena in DC / DC converters using type-2 fuzzy logic controller. Diagnostyka 2018; 19(3): 73-80. https://doi.org/10.29354/diag/93139.
  • 28. Gmurman VE. A guide to solving problems in probability theory and mathematical statistics. 2004. 407.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-02e54cb6-a0c3-4e7e-ae77-6f778f55950e
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