Towards Gaussian Process Models of Complex Rotorcraft Dynamics

Physical law based models (also known as white box models) are widely applied in the aerospace industry, providing models for dynamic systems such as helicopter flight simulators. To meet the criteria of real-time simulation, simplifications to the underlying physics sometimes have to be applied, leading to errors in the model's predictions. Grey-box models use both physics-based and data-based models. They have potential to reduce the difference between a simulator's and real rotorcraft's response. In the current work, a preliminary step to the grey-box approach, a machine learnt data-based, i.e 'black box' model is applied to the dynamic response of a helicopter. The machine learning methods used are probabilistic and can capture uncertainties associated with the model's prediction. In the current paper, machine learning is used to create a Gaussian Process (GP) non-linear autoregressive (NARX) model that predicts pitch, roll and yaw rate. The predictions are compared to a physical law based model created using FLIGHTLAB software. The GP outperforms the FLIGHTLAB model in terms of root mean squared error, when predicting the pitch, roll and yaw rate of a Bo105 helicopter.