Upset Prevention Research Articles

Model Predictive Control Based Washout Algorithm Design for Flight Simulator Upset Prevention and Recovery Training

Model Predictive Control Based Washout Algorithm Design for Flight Simulator Upset Prevention and Recovery Training

Effectiveness of Automatic Safety Systems in Reducing General Aviation Fatalities

General aviation accounts for 95% of fatal aviation accidents in the United States. Many of these fatalities may be preventable with the inclusion of advanced advisory or automatic aircraft safety systems; however, past research has rarely focused on the effect such systems could have if applied to general aviation. This paper addresses this gap by starting with proposed safety systems and scoring their effectiveness using detailed accident data rather than the more common approaches of analyzing accident categories or polling subject matter experts. Using a data-driven logical model, this paper evaluates four systems (terrain impact prevention, upset prevention, turbulence avoidance, and emergency landing) for potential to reduce fatalities. The scoring process appears in some detail, which starts with system requirements and National Transportation Safety Board data as inputs and results in expected fatal accident reductions. The analysis estimates that implementing all four systems may result in a 66.5% reduction in general aviation fatalities, with terrain impact prevention alone resulting in a possible 30.7% reduction. These results motivate immediate effort toward implementation and certification of aircraft safety systems into general aviation, not only to save the lives of hundreds of pilots and passengers a year, but also to prime small aircraft transportation for future developments.

Switched Motion Cueing Algorithm for Flight Simulator Upset Prevention and Recovery Training

Upset prevention and recovery training (UPRT) has been mandatorily required in the Level-D flight simulator by some civil aviation authority currently. Due to the limit motion capability of six-DOF motion platform, motion perception of pilot can’t be effectively satisfied with traditional classical motion cueing algorithm (MCA) during this kind of upset scenario training. In this paper, a switched MCA which optimized by GA was proposed. The switching mechanism ensured that MCA output optimal motion simulation signal to motion platform in different upset training phases. The motion command outputs, which may have sudden change during the transit of adjacent flight phases, were smoothed by a sigmoid function window. Simulation results showed that the proposed switched motion cueing algorithm had better motion replication performance and also made better usage for the motion platform’s workspace than the classical MCA.

Eye tracking as a debriefing tool in upset prevention and recovery training (UPRT) for general aviation pilots

Upset prevention and recovery training (UPRT) is intended to improve the ability of pilots to recognize and avoid situations that can lead to airplane upsets and to improve their ability to recover control of an airplane that has exceeded the normal flight envelope. To this end, a set of different training contents – from theoretical knowledge of aerodynamics and human factors to practice-based flight training – is necessary. In order to support the debriefing with an objective feedback, and because visual scanning is a core competence, two studies on subjective evaluation of aviation pilots – one conducted in a flight simulator and the other one in-flight – focussed on the practical application of eye tracking as a debriefing tool in UPRT. From a practitioner’s perspective, eye tracking appeared to be a useful method in terms of visualising instrument scanning techniques, supporting the instructor with objective debriefing material and fostering self-awareness in human processes. The discussion recommends adjusted UPRT instructor training and further improvements to eye tracking hardware and software.Practitioner Summary: The article focuses on pilot evaluations of eye tracking as a debriefing tool in UPRT and the identification of critical elements in its use. Eye tracking is a promising debriefing tool for UPRT. The discussion points to desirable improvements of eye tracking hardware and software as well as adjustments to instructor training that are pertinent.

확장칼만필터를 이용한 항공기 비정상 비행상황 판단 및 방지를 위한 실시간 대처법 연구

비정상 비행(Upset) 상황으로 인한 항공기 사고는 유인항공기와 무인항공기 모두에 치명적인 피해를 발생시킨다. 본 논문은 항공기의 비정상 비행상황에 대한 실시간 대처와 추가적인 위험상황을 방지하기 위한 기법을 연구한다. 먼저 확장칼만필터(Extended Kalman Filter) 방법을 이용해 얻게 되는 예측값과 센서를 통해 실제로 얻게 되는 측정값 사이의 차이를 모니터링하여 현재 항공기의 비정상 비행 진입 여부를 판단한다. 또한, 확장칼만필터의 시간 업데이트를 반복 연산하여 얻은 수 초 후의 예측값을 통해 항공기의 상태가 비정상 비행상황으로 진입할 것인지를 예측하여 사전에 판단할 수 있게 된다. 본 연구결과는 차세대 유무인 항공기의 비정상 비행 상태 진입 방지 시스템 구축을 위한 가교 역할을 할 것으로 사료된다.

Dealing With Unexpected Events on the Flight Deck: A Conceptual Model of Startle and Surprise

Objective:A conceptual model is proposed in order to explain pilot performance in surprising and startling situations.Background:Today’s debate around loss of control following in-flight events and the implementation of upset prevention and recovery training has highlighted the importance of pilots’ ability to deal with unexpected events. Unexpected events, such as technical malfunctions or automation surprises, potentially induce a “startle factor” that may significantly impair performance.Method:Literature on surprise, startle, resilience, and decision making is reviewed, and findings are combined into a conceptual model. A number of recent flight incident and accident cases are then used to illustrate elements of the model.Results:Pilot perception and actions are conceptualized as being guided by “frames,” or mental knowledge structures that were previously learned. Performance issues in unexpected situations can often be traced back to insufficient adaptation of one’s frame to the situation. It is argued that such sensemaking or reframing processes are especially vulnerable to issues caused by startle or acute stress.Conclusion:Interventions should focus on (a) increasing the supply and quality of pilot frames (e.g., though practicing a variety of situations), (b) increasing pilot reframing skills (e.g., through the use of unpredictability in training scenarios), and (c) improving pilot metacognitive skills, so that inappropriate automatic responses to startle and surprise can be avoided.Application:The model can be used to explain pilot behavior in accident cases, to design experiments and training simulations, to teach pilots metacognitive skills, and to identify intervention methods.

The Influence of Surprise on Upset Recovery Performance in Airline Pilots

ABSTRACTObjective: The aim of this study was to test if performance of airline pilots, in performing an aerodynamic stall recovery procedure, decreases when they are surprised, compared to when they anticipate a stall event.Background: New flight-safety regulations for commercial aviation recommend the introduction of surprise and startle in upset prevention and recovery training. This calls for more evidence on the effects of surprise on pilot performance, as well as methods to effectively induce surprise in training simulators.Method: The study took place in a motion-base simulator with a poststall aerodynamic model. Using a within-subjects design, the recovery performance of 20 pilots was tested in 2 conditions: 1 anticipated condition, and 1 surprise condition. In addition to flight parameters, subjective and physiological data relating to surprise and startle were measured.Results: Pilots had significantly more difficulties with adhering to the recovery procedure in the surprise condition compared to the anticipation condition. The subjective and physiological measures confirmed that the manipulation mainly increased surprise, and to a lesser extent also startle.Conclusion: The results suggest that pilots have more difficulty in managing an upset situation (i.e., an aerodynamic stall) when this situation is presented unexpectedly, underlining that upset prevention and recovery training should include elements of surprise.

Model-based failure management for aircraft control recovery

This paper describes the development and the performance analysis of an active failure management system for aircraft control upset prevention and recovery. The development of failure detection and system parameter identification algorithms that can reliably detect and isolate system failures with minimal disruption of normal operations constitutes the heart of the paper.