Nowadays dampers based on magnetorheological (MR) fluids are receiving significant attention specially for control of structural vibration and automotive suspensions systems. In most cases, it is necessary to develop an appropriate control strategy which is practically implementable when a suitable model for MR dampers is available. It is not a trivial task to model the dynamic of MR dampers because of their inherent non-linear and hysteretic dynamics. In this paper, a recursive lazy learning method based on neural networks is considered to model the MR damper behavior. The proposed method is validated by comparison with experimental obtained responses. Results show the estimated model correlates very well with the data obtained experimentally. The method proposed learns quickly that it is only necessarily a learning cycle, it can learn on-line and it is easy to select the network structure and calculate the model parameters. The model is validated using a set of experimental measurements corresponding to different current constant values ranging from 0 to 1.2 A. Results have shown the estimated model satisfactorily emulates the hysteretic and inherent non-linear behavior of the MR damper. There are main advantages gained with this new method with regards to previous proposed models. It is only necessarily a learning cycle so that it learns quickly, it is easy to select the network structure and a previous fitting of parameters is not necessary. Moreover, due to the fact that it is a recursive method, the model can be adjusted each time a new data are presented. Due to these advantages, this model can be readily used in the design of semi-active control algorithms.
Modeling of a magnetorheological damper by recursive lazy learning
International Journal of Non-Linear Mechanics ; 46 , 3 ; 479-485
2011
7 Seiten, 7 Bilder, 1 Tabelle, 31 Quellen
Aufsatz (Zeitschrift)
Englisch