Bridge State and Average Train Axle Mass Estimation for Adaptive Railway Bridges

Adaptive structures are equipped with sensors and actuators to counteract deformations caused by external loads. Concerning railway bridges, previous work has shown that active vibration damping allows to extend the service life. Trains as external loads represent the decisive influencing factor for bridge vibration and has to be taken into account when applying model-based control concepts. This article proposes a state and disturbance estimator (SDE) for bridge structures based on a moving point load train model and estimating the average train axle mass. The model employed for state and disturbance estimation is linear time variant, which allows use of an augmented Kalman filter. Estimability is analyzed based on the Fisher information and the proposed state and disturbance estimator (SDE) is systematically tested through simulations. A linear quadratic regulator is designed and combined with the proposed SDE to evaluate the closed-loop performance for damping the bridge vibrations during train crossing.