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Non-parametric UAV system identification with dependent Gaussian processes

A mathematical model for a complex system such as an Unmanned Aerial Vehicle (UAV) requires estimation of aerodynamic, inertial and structural properties of the many elements of the platform. This physical modeling approach is labor intensive and requires coarse approximations to be made in calculations. Similarly, models constructed through flight tests are only applicable to a narrow flight envelope and classical system identification approaches require prior knowledge of the model structure, which in some instance may only be partially known. To tackle these problems, we introduce a novel aircraft system identification method based on dependent Gaussian processes. The approach allows high fidelity nonlinear flight dynamic models to be constructed through flight testing. The proposed algorithm learns the system parameters as well as captures any dependencies between them. The method is demonstrated by generating a model of the force and moment coefficients for the Brumby MkIII UAV from real flight data. The learnt dynamic model identifies coupling between flight modes, provides an estimate of uncertainty, and is applicable to a broader range of the flight envelope.