Aerodynamic state estimation of a bio-inspired distributed sensing UAV at high angles of attack and sideslip

Abstract

Biological fliers’ remarkable manoeuvrability and robust flight control are aided by information from dense arrays of distributed flow sensors on their wings. Bio-inspired fixed-wing uncrewed aerial vehicles (UAVs) with a “flight-by-feel” control approach could mimic these abilities, allowing safe operation in cluttered urban areas. Existing work has focused on longitudinal parameter estimation and control at low angles of attack. This wind-tunnel study estimates both the longitudinal and lateral-directional aerodynamic states of a bio-inspired distributed pressure sensing UAV at angles of attack and sideslip up to 25° and 45°. Four span-wise strips of pressure sensors were found to show strong, location dependent variation with angle of sideslip across all angles of attack, indicating that distributed pressure sensing arrays can encode lateral-directional flow information. This was supported by the use of the pressure signals in estimator algorithms, which showed angle of sideslip estimation was possible with both a linear partial-least-squares regression-based estimator and a non-linear feed-forward artificial neural network estimator. The non-linear estimator could predict angle of sideslip with a lower error than the linear estimator, with a root-mean-square error (RMSE) of 0.70° for the former compared to 1.23° for the latter. They both showed good estimation of angle of attack, even in the post-stall regime, with an RMSE of 0.58° for the linear estimator and 0.54° for the non-linear estimator. These results show that pressure-based distributed sensing can capture a complete aerodynamic picture of a UAV, unlocking the potential of a“flight-by-feel” control system informed by the aerodynamic states of the vehicle across a wide range of aerodynamic conditions.

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Identifiers

ORCID for Sergio Araujo-Estrada: orcid.org/0000-0002-5432-5842

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