Fuzzy Model of a Linear Magnetorheological Fluid Damper

• Autorzy: Sapiński B. , Habdank-Wojewódzki S.
• Języki publikacji: EN

Abstrakty

EN

A fuzzy model of a linear magnetorheological (MR) damper based on expert knowledge is presented, as one of the alternatives in non-parametric approach to modelling of such dampers. It is explained why this method of MR damper modelling was selected and general principles of fuzzy logie are provided. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is then applied in training of the fuzzy model of MR damper, in the shape of Takagi-Sugeno-Kang (TSK) structure. The method of measurement data processing, model training the target fitting are also provided.

Słowa kluczowe

EN

magnetorheological fluid damper; fuzzy logic; modelling

• Wydawca: Wydawnictwa AGH
• Czasopismo: Mechanika / Akademia Górniczo-Hutnicza im. St. Staszica
• Rocznik: 2003
• Tom: T. 22, z. 3
• Strony: 417--425
• Opis fizyczny: Bibliogr. 10 poz., rys.

Twórcy

autor

Sapiński B.

• Department of Process Control, AGH University of Science and Technology, Cracow

autor

Habdank-Wojewódzki S.

Bibliografia

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• [3] Sapiński B.: Badania eksperymentalne charakterystyk mechanicznych liniowego tłumika magnetoreołogicznego. V Konferencja Metody Aktywne Redukcji Drgań i Hałasu, Krynica 2001, 257-268
• [4] Sapiński B.: Non-parametric Representations of MR Linear Damper Behavior. IUTAM Symposium on Dynamics of Advanced Materials and Smart Structures, Yonezawa, Japan, 2002, Kluwer Academic Publishers, 347-357
• [5] Sapiński B., Filuś J.: Analysis of Parametric Models of MR Linear Damper. Journal of Theoretical and Applied Mechanics, tom 1, zeszyt 2, 2003
• [6] Sapiński B.: Dynamic Characteristics of an Experimental MR Fluid Damper. Engineering Transactions (to be published)
• [7] Schurter K.C., Roschke P.N.: Fuzzy Modeling of a Magnetorheological Damper Using ANFIS. 9-th IEEE International Conference on Fuzzy Systems, 2000, 122-127
• [8] Yager R.R., Filev D.P.: Essential of Fuzzy Modeling and Control. John Wiley 1994
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• [10] Using MATLAB - Fuzzy Logic Toolbox. Natick, Massachusetts, The Math Works Inc. 1999