Adaptive UKF-Based Parameter Estimation for Bouc-Wen Model of Magnetorheological Elastomer Materials

Abstract

Structural system identification has attracted much attention in the structural dynamic field over the past decades. For identifying parameters of the inelastic response of a structure under ground shaking, the Kalman filter (KF) and unscented Kalman filter (UKF) have been used extensively. In this paper, numerical and experimental investigations were carried out to test the capabilities of square-root unscented Kalman filters (SRUKF) and adaptive square-root unscented Kalman filters (ASRUKF) for identifying the parameters of the nonlinear structural system, with the Bouc-Wen model applied to describe the nonlinear hysteresis of magnetorheological elastomer materials. A new method was proposed for parameter initial values estimation, which could ensure that the parameters in the constitutive equation be identified uniquely and thus reduce the influence of the initial error on the parameter estimation. The numerical investigation showed that the ASRUKF outperformed the SRUKF in both convergence speed and estimation accuracy. Furthermore, the ASRUKF was able to track the sudden change of the parameter whereas the SRUKF was not. The experimental results indicate that the estimated Bouc-Wen model through ASRUKF not only presents a good match with the experimental data for a specific input but also keeps physical properties that are inherent to the real data, independently of the exciting input.