Failure-Proof Algorithm for Angle of Attack Estimation

For a pilot, the angle of attack (AoA) estimation may represent a possibility to cross-check the traditional cockpit indicators and also to obtain information whose benefits are widely recognized in many inflight situations. This paper proposes an AoA algorithm—named ACF—capable of detecting fast changes in the actual signal, that is a useful stall prevention capability, for example. The solution is based on the complementary filtering methodology and does not involve any airdata sensor output, with consequent saving of weight and cost reduction. In particular, it does not use the airspeed measurement, easily subject to failure and has been identified as the root cause of several fatal accidents. Consequently, the ACF shows the same (high) reliability as the used inertial sensors. Furthermore, the ACF algorithm requires neither information on the aircraft aerodynamic coefficients nor forecast wind information to be preloaded onboard, thus being time and cost effective. The ACF is also characterized by a low computational load, only needing a few dozen of algebraic operations. In this work, the ACF performances have been assessed using both a detailed simulation environment and real flight data. In the former case, an airliner following a sample maneuver is assumed, and a mean root mean square (RMS) less than 3° is guaranteed under all possible conditions of wind, gust, and turbulence. In the latter case, data collected in two different general aviation flights are used, providing a mean RMS of less than 0.3°.

During piloting, the angle of attack (AoA) readings may represent a possibility to cross-check the traditional indicators and to obtain information whose benefits are recognized in many inflight situations. Unfortunately, the AoA sensors are exposed to several possible failures, often common to the other airdata such as airspeed and altitude. In addition, especially for small aerial vehicles, the systems cannot accommodate direct sensors due to size, weight, and power constraints. For these reasons, having an AoA sensorless estimation would be extremely important. A software solution (ACF) is proposed to estimate the AoA without using any external sensor, saving weight and costs, and with a strong improvement of reliability since the angle vanes—and especially the air probes—are generally identified as the root cause of several inflight accidents. Due to the fact that the ACF algorithm relies on inertial measurements only, it could even be implemented in a pilot’s personal device because it does not require any tuning or flight calibration. Furthermore, in spite of its simplicity, it can achieve good accuracy by detecting—in any weather and regardless of particular airplane or maneuver—fast changes in the actual AoA that may endanger the safety of the flight.