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Synthesis water level control by fuzzy logic

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: This paper focuses on evolving of two types fuzzy and classical PID liquid level controller and examining whether they are better able to handle modelling uncertainties. A two stage strategy is employed to design the synthesis fuzzy and classical PID controller with the process of the first and second order and implements disorder (quadratic function). Design/methodology/approach: The synthesis of fuzzy and classical PID liquid level controller was realized with the HP laptop 6830s Compaq NA779ES, software Matlab/Simulink 2008b, FIS (Fuzzy Inference System) soft logical tool, input-output unit 500 Dragon Rider and ultrasonic sensor. Using the simulation program Matlab/Simulink/FIS we simulate the operation of fuzzy and classical controller in the liquid level regulating cycle and made a comparison between fuzzy and classical controller functioning. Findings: From the responses to step fuzzy and classical controller for first-order process shows that the actual value of the controlled variable takes the value one. Fuzzy and classical PID controller does not allow control derogation, which is also inappropriate for fuzzy and classical control cycle with incorporating disturbance. Classical PID controller in the first-order process provides short-term regulation, such as fuzzy PID controller. In fuzzy control cycle with fuzzy PID controller and incorporating disturbance in the process of second-order the control cycle is stable and at certain predetermined parameters (integral gain) a control does not allow deviations. Research limitations/implications: In future research, the robustness of the fuzzy logic controller will be investigated in more details. Practical implications: Using fuzzy liquid level controller can reduce power consumption by 25%. Originality/value: Fuzzy logic controller is useful in applications of nonlinear static characteristic, where classical methods with usually classical PID controllers cannot be a satisfactory outcome.
Rocznik
Strony
204--210
Opis fizyczny
Bibliogr. 15 poz., rys., tab.
Twórcy
autor
  • Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia
autor
  • Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia
autor
  • Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia
autor
  • Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia
autor
  • Faculty of Agriculture and Life Sciences, University of Maribor, Pivola 10, 2311 Hoce, Slovenia
Bibliografia
  • [1] D. Dzonlagic, Basics of the design of fuzzy control systems, Faculty of Electrical Engineering and Computer Science, Maribor, 1995.
  • [2] C.C. Lee, Fuzzy logic in control systems: fuzzy logic controller-parts 1 and 2, IEEE Transactions on Systems, Man and Cybernetics 20/2 (1990) 404-435.
  • [3] L.A. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, IEEE Transactions on Systems, Man, and Cybernetics 3/1 (1973) 28-44.
  • [4] M. Sugeno, Industrial applications of fuzzy control, Elsevier Science Pub, 1985.
  • [5] L. Reznik, O. Ghanayem, A. Bourmistrov, PID plus fuzzy controller structures as a design base for industrial applications, Engineering Applications of Artificial Intelligence 13 (2000) 419-430.
  • [6] E.H. Mamdani, Advances in the linguistic synthesis of fuzzy controllers, International Journal of Man Machine Studies 8 (1976) 669-678.
  • [7] E.H. Mamdani, Applications of fuzzy logic to approximate reasoning using linguistic synthesis, IEEE Transactions on Computers 26/12 (1977) 1182-1191.
  • [8] J.L. Castro, J.J. Castro-Schez, J.M. Zurita, Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems, Fuzzy Sets and Systems 101/3 (1999) 331-342.
  • [9] T. Iijima, Y. Nakajima, Y. Nishiwaki, Application of fuzzy logic control system for reactor feed-water control, Fuzzy Sets and Systems 74/1 (1995) 61-72.
  • [10] P. Vindis, B. Mursec, M. Janzekovic, F. Cus, Processing of soybean meal into concentrates and testing for Genetically Modified Organism (GMO), Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 507-510.
  • [11] P. Vindis, B. Mursec, C. Rozman, M. Janzekovic, F. Cus, Biogas production with the use of mini digester, Journal of Achievements in Materials and Manufacturing Engineering 26/1 (2008) 99-102.
  • [12] P. Vindis, B. Mursec, M. Janzekovic, F. Cus, The impact of mesophilic and thermophilic anaerobic digestion on biogas production, Journal of Achievements in Materials and Manufacturing Engineering 36/2 (2009) 192-198.
  • [13] D. Stajnko, M. Janzekovic, B. Mursec, P. Vindis, F. Cus, The efficiency of different machines for controlling of western corn rootworm adults, Journal of Achievements in Materials and Manufacturing Engineering 40/1 (2010) 79-86.
  • [14] P. Vindis, B. Mursec, M. Janzekovic, D. Stajnko, F. Cus, Anaerobic digestion of maize hybrids for methane production, Journal of Achievements in Materials and Manufacturing Engineering 40/1 (2010) 87-94.
  • [15] U. Zuperl, F. Cus, B. Mursec, A. Ploj, A Hybrid analytical-neural network approach to the determination of optimal cutting conditions, Journal of Materials Processing Technology 157/158 (2004) 82-90.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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