Bálint Patartics(1*), Bálint Kiss(2)

(*) Corresponding Author

The Application of Unknown Input Estimators to Damp Load Oscillations of Overhead Cranes


This paper focuses on the development of state estimation methods for mechanical systems with uncertain frictional parameters. The goal of the study is to provide reliable angle estimation for state-feedback-based crane control solutions, designed to reduce load sway. Cranes are underactuated systems, usually unequipped with the sensors necessary to measure the swinging angle, therefore the damping of their oscillatory behaviour is a challenging task. Two estimators are proposed for the calculation of the unmeasured states. One is based on an ’unknown input Kalman filter’ (UIKF), the other applies the ’unscented Kalman filter’ (UKF) with load prediction. Simulation results are provided to demonstrate the accuracy of the algorithms.


overhead crane; state estimation; nonlinear systems; unknown inputs; Kalman filter

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