Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned

Flight Test of an Adaptive Controller and Simulated Failure/Damage on the NASA NF-15B

The method of flight-testing the Intelligent Flight Control System (IFCS) Second Generation (Gen-2) project on the NASA NF-15B is herein described. The Gen-2 project objective includes flight-testing a dynamic inversion controller augmented by a direct adaptive neural network to demonstrate performance improvements in the presence of simulated failure/damage. The Gen-2 objectives as implemented on the NASA NF-15B created challenges for software design, structural loading limitations, and flight test operations. Simulated failure/damage is introduced by modifying control surface commands, therefore requiring structural loads measurements. Flight-testing began with the validation of a structural loads model. Flight-testing of the Gen-2 controller continued, using test maneuvers designed in a sequenced approach. Success would clear the new controller with respect to dynamic response, simulated failure/damage, and with adaptation on and off. A handling qualities evaluation was conducted on the capability of the Gen-2 controller to restore aircraft response in the presence of a simulated failure/damage. Control room monitoring of loads sensors, flight dynamics, and controller adaptation, in addition to postflight data comparison to the simulation, ensured a safe methodology of buildup testing. Flight-testing continued without major incident to accomplish the project objectives, successfully uncovering strengths and weaknesses of the Gen-2 control approach in flight

Implementation of an Adaptive Controller System from Concept to Flight Test

The National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) is conducting ongoing flight research using adaptive controller algorithms. A highly modified McDonnell-Douglas NF-15B airplane called the F-15 Intelligent Flight Control System (IFCS) was used for these algorithms. This airplane has been modified by the addition of canards and by changing the flight control systems to interface a single-string research controller processor for neural network algorithms. Research goals included demonstration of revolutionary control approaches that can efficiently optimize aircraft performance for both normal and failure conditions, and to advance neural-network-based flight control technology for new aerospace systems designs. Before the NF-15B IFCS airplane was certified for flight test, however, certain processes needed to be completed. This paper presents an overview of these processes, including a description of the initial adaptive controller concepts followed by a discussion of modeling formulation and performance testing. Upon design finalization, the next steps are: integration with the system interfaces, verification of the software, validation of the hardware to the requirements, design of failure detection, development of safety limiters to minimize the effect of erroneous neural network commands, and creation of flight test control room displays to maximize human situational awareness

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Predicting Pilot Success Using Machine Learning

The United States Air Force has a pilot shortage. Unfortunately, training an Air Force pilot requires significant time and resources. Thus, diligence and expediency are critical in selecting those pilot candidates with a strong possibility of success. This research applies multivariate and statistical machine learning techniques to pilot candidates pre-qualification test data and undergraduate pilot training results to determine whether there are selected pre-qualification tests or specific training evaluations that do a \best job of screening for successful pilot training candidates and distinguished graduates. Flight experience, both during training and otherwise, indicates pilot training completion and performance

Aeronautical enginnering: A cumulative index to a continuing bibliography (supplement 312)

This is a cumulative index to the abstracts contained in NASA SP-7037 (301) through NASA SP-7073 (311) of Aeronautical Engineering: A Continuing Bibliography. NASA SP-7037 and its supplements have been compiled by the Center for AeroSpace Information of the National Aeronautics and Space Administration (NASA). This cumulative index includes subject, personal author, corporate source, foreign technology, contract number, report number, and accession number indexes

Joint University Program for Air Transportation Research, 1991-1992

This report summarizes the research conducted during the academic year 1991-1992 under the FAA/NASA sponsored Joint University Program for Air Transportation Research. The year end review was held at Ohio University, Athens, Ohio, June 18-19, 1992. The Joint University Program is a coordinated set of three grants sponsored by the Federal Aviation Administration and NASA Langley Research Center, one each with the Massachusetts Institute of Technology (NGL-22-009-640), Ohio University (NGR-36-009-017), and Princeton University (NGL-31-001-252). Completed works, status reports, and annotated bibliographies are presented for research topics, which include navigation, guidance and control theory and practice, intelligent flight control, flight dynamics, human factors, and air traffic control processes. An overview of the year's activities for each university is also presented

A Methodology to Improve the Proactive Mitigation of Helicopter Accidents Related to Loss of Tail Rotor Effectiveness

Loss of tail rotor effectiveness (LTE) has been recognized to be a major contributing factor in several helicopter accidents where pilots lost directional control. However, it has been noticed that different definitions of this phenomenon exist in the rotorcraft community. Further, the somewhat imprecise representation of LTE in some flight training simulators has led to its low awareness, placing pilots at a much higher risk for potential accidents. One significant method to specifically address those gaps and support rotorcraft safety involves the proactive mitigation of LTE via the analysis of flight data within the Helicopter Flight Data Monitoring (HFDM) program. Through this program, the pilots receive constant flight evaluation reports to promote improved LTE risk evaluations. The main method used for flight data analysis is the detection of safety metrics, i.e., predefined hazardous flight conditions. Nevertheless, a sufficiently reliable LTE safety metric still does not exist, leading to false or missed detections that degrade the quality of the overall safety analysis. The objective of this thesis is to formulate a methodology to enhance the detection capability of the proximity to LTE within the HFDM program. This promotes the awareness of LTE within the rotorcraft community while supporting the proactive mitigation of helicopter accidents related to this critical helicopter safety threat. An alternative approach is used to develop a more reliable LTE safety metric, using a combination of physics-based simulations and machine learning techniques. First, a physics-based investigation is performed to enhance the understanding of the nature of the LTE. A more comprehensive LTE definition is proposed and analyzed, including three different aspects that can lead to LTE behavior, i.e., loss of weathercock stability, running out of pedal (tail rotor collective) for trim, and tail rotor vortex ring state. The modeling of the flight dynamics of each phenomenon is individually analyzed to ensure an accurate physics-based representation of LTE. Further, the parameters that support the detection of LTE are investigated to enable the recognition and classification of each LTE phenomenon in simulation results. Ultimately, a physics-based investigation of the aircraft flight envelope is combined with the application of supervised learning techniques to develop the predictive models of the different LTE phenomena. This provides the operator with a physics-based LTE safety metric designed to detect the proximity to LTE without the need for a simulation model. The methodology is implemented using a generic nonlinear helicopter simulation model. To verify the enhanced capabilities of the final methodology, the physics-based LTE safety metric is compared against the LTE metric currently used within the HFDM program. The results confirm the improved detection of the proximity to LTE, validating the overarching hypothesis of this research and satisfying the research objective.Ph.D

Analysis and evaluation of the pilot attentional model

Pendant les opérations de vol, les pilotes sont exposés à une variété de conditions émotionnelles, mentales et physiques qui peuvent affecter leurs performances et leur attention. Par conséquent, il est crucial de surveiller leur charge de travail et leurs niveaux d'attention pour maintenir la sécurité et l'efficacité de l'aviation, notamment dans les situations d'urgence. La charge de travail fait référence aux exigences cognitives et physiques imposées aux pilotes lors d'un vol. Des niveaux élevés de charge de travail peuvent entraîner une fatigue mentale, une attention réduite et une surcharge cognitive, ce qui peut entraver leur capacité à effectuer leurs tâches de manière efficace et efficiente. L'attention est un processus cognitif complexe qui limite la capacité de se concentrer et de comprendre tout en même temps. Dans les tâches de traitement de l'information visuelle, la vision humaine est la principale source du mécanisme d'attention visuelle. Le mode de distribution de l'attention d'un pilote a un impact significatif sur la quantité d'informations qu'il acquiert, car la vision est le canal le plus critique pour l'acquisition d'informations. Une mauvaise allocation des ressources attentionnelles peut amener les pilotes à négliger ou à oublier des paramètres spécifiques, ce qui entraîne des risques graves pour la sécurité des aéronefs. Ainsi, cette étude vise à étudier les niveaux d'attention des pilotes lors d'une procédure de décollage simulée, en mettant l'accent particulièrement sur les périodes critiques telles que les pannes de moteur. Pour ce faire, l'étude examine s'il existe une corrélation entre la dilatation de la pupille, mesurée à l'aide de la technologie de suivi oculaire, et les niveaux d'engagement, mesurés à l'aide de l'EEG. Les résultats indiquent que les changements de taille de la pupille sont effectivement corrélés aux changements d'activité de l'EEG, suggérant que la dilatation de la pupille peut être utilisée comme un indicateur fiable de l'engagement et de l'attention. Sur la base de ces résultats, la dilatation de la pupille et l'EEG peuvent être utilisés en combinaison pour examiner de manière globale le comportement des pilotes, car les deux mesures sont des indicateurs valides de l'engagement et de la charge cognitive. De plus, l'utilisation de ces mesures peut aider à identifier les périodes critiques où les niveaux d'attention des pilotes nécessitent une surveillance étroite pour garantir la sécurité et l'efficacité de l'aviation. Cette étude met en évidence l'importance de surveiller la charge de travail et les niveaux d'attention des pilotes et recommande d'utiliser les mesures de dilatation de la pupille et d'EEG pour évaluer la charge cognitive et l'engagement d'un pilote pendant les opérations de vol, améliorant ainsi la sécurité et l'efficacité de l'aviation.During flight operations, pilots are exposed to a variety of emotional, mental, and physical conditions that can affect their performance and attention. Therefore, it is crucial to monitor their workload and attention levels to maintain aviation safety and efficiency, particularly in emergency situations. Workload refers to the cognitive and physical demands placed on pilots during a flight. High levels of workload can lead to mental fatigue, reduced attention, and cognitive overload, which can hinder their ability to perform their tasks effectively and efficiently. Attention is a complex cognitive process that limits the ability to focus and comprehend everything simultaneously. In visual information processing tasks, human vision is the primary source of the visual attention mechanism. A pilot's attention distribution mode significantly impacts the amount of information they acquire, as vision is the most critical channel for information acquisition. Improper allocation of attention resources can cause pilots to overlook or forget specific parameters, resulting in severe risks to aircraft safety. Thus, this study aims to investigate pilots' attention levels during a simulated takeoff procedure, with a specific focus on critical periods such as engine failures. To achieve this, the study examines whether there is a correlation between pupil dilation, measured using eye-tracking technology, and engagement levels, measured using EEG. The results indicate that changes in pupil size are indeed correlated with changes in EEG activity, suggesting that pupil dilation can be used as a reliable indicator of engagement and attention. Based on these findings, pupil dilation and EEG can be used in combination to comprehensively examine pilot behavior since both measures are valid indicators of engagement and cognitive workload. Furthermore, using these measures can help identify critical periods where pilots' attention levels require close monitoring to ensure aviation safety and efficiency. This study emphasizes the significance of monitoring pilots' workload and attention levels and recommends using pupil dilation and EEG measures to assess a pilot's cognitive workload and engagement during flight operations, ultimately enhancing aviation safety and efficiency

Low airspeed systems for the naval SH-60 Seahawk aircraft

Pitot-static systems have long been used to measure helicopter airspeed. The Pitot-static system is inaccurate at low airspeeds (below 40 knots) due to the limited sensitivity of the sensor and interference of rotor down wash. Additionally, the Pitot-static system only measures unidirectional airspeed and unlike its fixed wing counterparts the helicopter is not limited to flight in one direction. With the changing roles of the US Navy Seahawk it is imperative that the pilot and aircrew have all the information necessary to safely complete the mission and prolong the life of the aircraft and dynamic components. With the addition of a dipping sonar to the remanufactured SH-60B aircraft (designated SH- 60R) and the conduct of combat search and rescue mission in the Navy\u27s Seahawks the aircraft will spend more time in a hover and will be flown more aggressively than in the past. This thesis examiness the advantages of incorporating a low airspeed system into the modem helicopter, in particular the SH-60 Seahawk. The author examines the low airspeed sensors and systems currently available and gives a brief description of each system\u27s operation. The author examines the challenges of installing a low airspeed sensor onto the SH-60 Seahawk. The author has determined that either a laser velocimeter or an analytical neural network system would be the best approach for a low airspeed system for the SH-60 Seahawk. The author recommends a combined approach be taken to develop both the laser velocimeter and analytical neural network, and incorporate the best system after further flight testing