Energy state prediction methods for airplane state awareness

Abstract

The lack of aircraft state awareness has been one of the leading causal and contributing factors in aviation accidents. Many of these accidents were due to flight crew's inability to understand the automation modes and properly monitor the aircraft energy and attitude state. The capability of providing flight crew with improved airplane state awareness (ASA) is essential in ensuring aviation safety. This paper focusses on predictive alerting methods to achieve improved ASA and describes the methods used to predict (a) stall and overspeed conditions, (b) high-and-fast conditions, (c) low-and-slow conditions, (d) unstable approach conditions, and (e) automation mode transitions. The proposed method estimates and subsequently predicts the aircraft state based on (i) aircraft state related information output by the onboard avionics, (ii) the configuration of the aircraft, (iii) appropriate aircraft dynamics models of both the active modes and the modes to which can be transitioned via simple pilot actions, and (iv) accurate models of the uncertainty of the dynamics and sensors. Onboard avionics inputs include measurements from onboard navigation systems such as global navigation satellites systems (GNSS), inertial navigation systems, and air data. This paper provides a detailed description of the prediction algorithms, the predictive alerting display concepts, and some test results based on flight data collected during a recent NASA flight simulator study in which eleven commercial airline crews (22 pilots) completing more than 230 flights.