Magnetorheological (MR) Dampers offer rapid variation in damping properties, making them ideal in semi-active control of structures. They potentially offer highly reliable operation and can be viewed as fail safe, i...Magnetorheological (MR) Dampers offer rapid variation in damping properties, making them ideal in semi-active control of structures. They potentially offer highly reliable operation and can be viewed as fail safe, in that in the worst case, they become passive dampers. Perfect understanding of the response is necessary when implementing these in operation in conjunction with a control mechanism. There are many models used to predict the behavior of MR dampers. One of these is the Bouc-Wen model. It is extremely popular as it is numerically tractable, very versatile and can exhibit a wide range of hysteretic behavior. It is necessary to first identify the characteristic parameters of the model before response prediction is possible. However, characteristic parameters identification of the Bouc-Wen model needs an experimental base, which has its own limitations. The extraction of these characteristic parameters by trial and error and optimization techniques leaves significant difference between observed and simulated results. This paper deals with a new approach to extract characteristic parameters for the Bouc-Wen model.展开更多
文摘Magnetorheological (MR) Dampers offer rapid variation in damping properties, making them ideal in semi-active control of structures. They potentially offer highly reliable operation and can be viewed as fail safe, in that in the worst case, they become passive dampers. Perfect understanding of the response is necessary when implementing these in operation in conjunction with a control mechanism. There are many models used to predict the behavior of MR dampers. One of these is the Bouc-Wen model. It is extremely popular as it is numerically tractable, very versatile and can exhibit a wide range of hysteretic behavior. It is necessary to first identify the characteristic parameters of the model before response prediction is possible. However, characteristic parameters identification of the Bouc-Wen model needs an experimental base, which has its own limitations. The extraction of these characteristic parameters by trial and error and optimization techniques leaves significant difference between observed and simulated results. This paper deals with a new approach to extract characteristic parameters for the Bouc-Wen model.