State Estimation Using Different Disturbance Models for Adaptive Railway Bridges

Adaptive structures are equipped with sensors and actuators to counteract deformations and vibrations caused by external loads. For railway bridges, active control can be used to reduce vibration amplitudes to extend the service life of the structure, thereby increasing resource and emissions efficiency. Model-based control of bridge structures requires knowledge of the structural state and the external disturbance. This paper compares bridge state estimators (Kalman filters), that get no/full information about the disturbance or include disturbance models of different complexity in the estimation model with respect to the achievable estimation performance. It is shown that the disturbance cannot be neglected, however it is sufficient to take the average train axle weights as average mass (AM)-moving point load (MPL) model into account, while a more complex disturbance model does not improve the estimation performance. Hence, a state and disturbance estimator based on the AM-MPL model is proposed using an Augmented Kalman filter.