A Kalman-based identification approach for distributed aerodynamic loads on a rotating blade

The rotating blades of the helicopter are constantly interacting with the external fluid generating vibratory loads. These excitations are then transmitted to the rotor hub and can lead to failures in the main rotor system. The knowledge or prediction of the aerodynamic loads become thus of great importance for design and failure prevention. Several experiment-based and model-based techniques have been presented in literature, but given the complexity in helicopter modelling, high accuracy can only be reached if a large amount of sensor data and/or a high-fidelity numerical model is available. This contribution focuses on the usage of the Kalman filtering technique for rotor load estimation. The filter presents two main advantages: i) usage of a minimum set of sensors; ii) compensation of a low-fidelity model by accounting for sensor and model uncertainties. The problem of state and load estimation is addressed in this paper on a rotating helicopter blade through a numerical example. Numerical results show an accurate state reconstruction with respect to the selected sensor layout and model uncertainties. The distributed aerodynamic loads can be accurately reconstructed in post-processing.