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Design of three control algorithms for an averaging tank with variable filling

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Języki publikacji
EN
Abstrakty
EN
An averaging tank with variable filling is a nonlinear multidimensional system and can thus be considered a complex control system. General control objectives of such object include ensuring stability, zero steady-state error, and achieving simultaneously shortest possible settling time and minimal overshoot. The main purpose of this research work was the modeling and synthesis of three control systems for an averaging tank. In order to achieve the intended purpose, in the first step, a mathematical model of the control system was derived. The model was adapted to the form required to design two out of three planned control systems by linearization and reduction of its dimensions, resulting in two system variants. A multivariable proportional-integral-derivative (PID) control system for the averaging tank was developed using optimization for tuning PID controllers. State feedback and output feedback with an integral action control system for the considered control system was designed using a linear-quadratic regulator (LQR) and optimization of weights. A fuzzy control system was designed using the Mamdani inference system. The developed control systems were tested using theMATLAB environment. Finally, the simulation results for each control algorithm (and their variants) were compared and their performance was assessed, as well as the effects of optimization in the case of PID and integral control (IC) systems.
Rocznik
Strony
136--150
Opis fizyczny
Bibliogr. 19 poz., rys., tab., wykr.
Twórcy
  • Faculty of Electrical and Control Engineering, Gdańsk University of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
  • Faculty of Electrical and Control Engineering, Gdańsk University of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
Bibliografia
  • 1. Spellman FR. Handbook of Water and Wastewater Treatment Plant Operations. CRC Press LLC, Boca Raton, FL. 2003.
  • 2. Astrom KJ, Hagglund T. PID Controllers: Theory, Design and Tuning. 2nd ed. Instrument Society of America, Research Triangle, NC. 1995.
  • 3. Rojas–Moreno A, Parra–Quispe A. Design and Implementation of a Water Tank Control System Employing a MIMO PID Controller. Fac-ulty of Electrical and Electronic Engineering, National University of Engineering Lima. 2008.
  • 4. Johansson KJ. The Quadruple-Tank Process: A Multivariable Labor-atory Process with an Adjustable Zero. IEEE Transactions On Con-trol Systems Technology 2000;8(3): 456-465.
  • 5. Saeed Q, Uddin V, Katebi R. Multivariable Predictive PID Control for Quadruple Tank, World Academy of Science, Engineering and Tech-nology. 2010;12: 861-866.
  • 6. Meenatchi Sundaram S, Venkateswaran PR. Smith Predictor Imple-mentation of a High Dead Time Interacting Tank Process. Interna-tional Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), 27-28 July, Bhubaneswar, India. 2018.
  • 7. Janani S. Design of Integral Constant State Feedback Controller Using Ackermann’s Function, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE). 2014;9(1): 58-63.
  • 8. Bojan-Dragos C, Hedrea E, Precup R, Szedlak-Stinean A, Roman R. MIMO Fuzzy Control Solutions for the Level Control of Vertical Two Tank Systems. Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), SCITEPRESS, 29-31 July, Prague, Czech Republic. 2019: 810-817.
  • 9. Berk P, Stajnko D, Vindis P, Mursec B, Lakota M. Synthesis water level control by fuzzy logic. Journal of Achievements in Materials and Manufacturing Engineering. 2011; 45(2): 204-210.
  • 10. Kolankowski M, Piotrowski R. Synthesis of a state feedback control-ler for an averaging tank with variable filling. XXVI Scientific and Technical Conference Automation - News and Perspectives – AU-TOMATION 2022, 25-27 May, Warsaw, Poland (in print). 2022.
  • 11. Close CM, Frederick DK, Newell JC. Modeling and analisys of dy-namic systems. 3rd ed. John Wiley & Sons, Hoboken, NJ. 2017.
  • 12. Skogestad S, Postlethwaite I. Multivariable Feedback Control: Analy-sis and design. 2nd edition. John Wiley & Sons, Hoboken, NJ. 2005.
  • 13. fminmax, https://se.mathworks.com/help/optim/ug/fminimax.html, (access: 18.10.2021).
  • 14. Naidu DS. Optimal Control Systems. CRC Press, Boca Raton, FL. 2003.
  • 15. Kwakernaak H, Sivan R. Linear Optimal Control Systems. John Wiley & Sons, Inc., Hoboken, NJ. 1972.
  • 16. Murray RM. Optimization-Based Control. California Institute of Tech-nology, Pasadena, CA. 2008.
  • 17. Passino KM, Yurkovich S. Fuzzy Control. Addison Wesley Longman, Inc, Menlo Park, CA. 1998.
  • 18. Jantzen J. Foundations of Fuzzy Control. John Wiley & Sons, Inc., Hoboken, NJ. 2007.
  • 19. Mamdani EH. Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the Institution of Electrical Engineers. 1974;121(12): 1585–1588.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e72ef8ab-cc4b-4aa9-b0bd-7ba7801db9ae
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