RECURSIVE MODELING OF A MAGNETO-RHEOLOGICAL DAMPER

This paper proposes the use of three recursive system identification techniques for modeling a magneto-rheological (MR) damper. The results of the three models are compared to one another and to two parametric models that have been commonly used in the literature for modeling these dampers. An experimental set-up using an MR damper, that is fabricated in-house, is built and used for data collection. The collected data is used for system identification at varying input conditions and the results from the system identification models are compared to the measured data as well as to the parametric models. It is seen that while the parametric models work well within limited bounds of input variables, these models cannot be used outside the range of these bounds since any significant change in the inputs or operating conditions requires a new characterization of the model parameters. The recursive system identification models, instead, continuously update the model parameters as and when data becomes available, as demonstrated by the three models presented in this paper. The determination of the regressor can present a significant challenge in the implementation of recursive models; this paper uses an iterative method coupled with statistical measures in order to establish the regressor that is then used for all three system identification models. The advantages of the recursive models are conclusively established by a lower root mean square error (RMSE) and a better representation of the hysteretic and saturation phenomena exhibited by the MR damper, in addition to an improved model tracking. This provides an inherent advantage over the parametric models, thereby making the recursive models specifically conducive to adaptive control algorithms.