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Advisor(s)
Abstract(s)
Numerical modelling of magneto-rheological (MR) dampers based on parametric
models constitutes one of the main methodologies to simulate the response of these
actuators. However, the highly non-linear nature of these devices and also their
inherent rheological behaviour make this modelling approach harsh and complicated
hindering the development of simple numerical models. Usually complex parametric
models comprising numerous parameters are required to achieve a reliable and
accurate representation of the hysteretic behaviour of MR dampers. On the other
hand, non-parametric models seems to be an alternative modelling approach that can
deal with the complex non-linear behaviour of MR dampers without the need of to
define or identify a large number of model parameters. In this context, this paper
provides detailed information about a non-parametric technique based on an
adaptive neuro-fuzzy inference system (ANFIS) to create neuro-fuzzy models for
MR dampers. An ANFIS is used to optimize a fuzzy inference system by training a
family of membership functions in accordance with a predetermined input and
output data set related with the damper behaviour. This data optimization algorithm
presents the advantage of providing automatic tuning of a fuzzy inference system to
relate the device inputs (mechanical excitations and operating currents) to obtain the
desired damping force output. Initially, the background and basic concepts of fuzzy
modelling with an ANFIS algorithm are described. General guidelines are also
provided to improve the optimization procedure with this type of modelling
technique. A framework for modelling MR dampers with ANFIS was implemented
and its effectiveness in simulating the response of a commercial MR damper was
verified with both numerical training data. The results obtained with the resultant
neuro-fuzzy model are compared with those of experimental tests and also with an
established parametric model (i.e., the modified Bouc-Wen model).
Description
Keywords
Semi-active control MR damper Structural control Fuzzy control Neuro-fuzzy Dynamics
Citation
Braz-César, M.T.; Oliveira, Kellie; Barros, Rui (2015). Neuro-fuzzy modelling of magneto-rheological dampers. In Fourth International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering. Stirlingshire, Scotland.
Publisher
Civil Comp Press