Cheeseman Award Paper: Damper Model Identification Using Hybrid Physical and Machine Learning Based Approach

Abstract:
In this paper the identification of a time domain model of a helicopter main rotor lead-lag damper is discussed. Previous studies have shown that lead-lag dampers have a significant contribution to the overall aircraft dynamics, therefore an accurate damper model is essential to predict complex phenomena such as, instabilities, limit cycles, etc. Due to the inherently nonlinear dynamics and the complex internal architecture of these components, the model identification can be a challenging task. In this paper, a hybrid physical/machine learning based approach, has been used to identify a damper model based on experimental test data. The model, called grey box, consists of a combination of a white box, i.e. a physical model described by differential equations and a black box, i.e. regression numerical model. The white box approximates the core physical behaviour of the damper while the black box improves the overall accuracy by capturing the complex dynamic not included in the white box. The paper shows that, at room temperature, the grey box is able to predict the damper force when either a multi-frequency harmonic or a random input displacement is imposed. The model is validated up to 20 Hz and for the entire damper dynamic stroke.

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