Parameter Estimation for the Tuned Liquid Damper Model Based on Robust Extended Kalman Filter

The Tuned Liquid Damper (TLD) method offers a practical and cost-effective solution for seismic design. Accurate modeling of the TLD system’s dynamic behavior is crucial for optimizing its performance. In this study, the nonlinear dynamics of the TLD system are characterized using the Housner model, with parameters estimated via a nonlinear state estimation approach. To address challenges associated with model discretization and unknown noise processes, we introduce a Robust Extended Kalman Filter (REKF) that incrementally incorporates uncertainties to more accurately capture system dynamics. The proposed method is evaluated through real-time hybrid simulation, employing seismic input signals from the El Centro and Hachinohe ground motions. Comparative analyses indicate that the robust algorithm achieves superior parameter estimation relative to conventional methods, with estimated parameters closely aligning with reference values and resulting in minimal relative error. This work underscores the efficacy of robust algorithms in TLD vibration response analysis and presents a promising approach for dynamic modeling and seismic performance optimization.