Coupled state-input-parameter estimation for structural dynamics through Kalman filtering

In many practical structural applications, unknown states, inputs and parameters are present. However, most methods require one or more of these variables to be known in order to estimate the other(s). In this research an estimation technique which employs physical models is proposed to perform coupled state/input/parameter estimation. In order to obtain a modeling technique which allows the estimation of a wide range of parameters in a generic fashion at a minimal computational cost (even real-time), the use of a parametric model reduction technique is proposed. The reduced model is coupled to an extended Kalman filter (EKF) with augmented states for the unknown inputs and parameters. This leads to a very efficient framework for estimation in structural dynamics problems. The proposed methodology is validated experimentally on a cantilever beam with variable length. The approach is shown to be easy to tune and provides good results with different measurement methods.