Crew Systems and Aviation Operations

Page 260. Pilot Fault Management Performance: An Evaluation of Assistance Paul C. Schutte and Anna C. Trujillo NASA Langley Research Center INTRODUCTION Human error has been attributed to over 70% of all aircraft accidents (Boeing, 1996). ...

ABSTRACT Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator’s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.

ABSTRACT • If aircraft dynamics become more sluggish and unpredictable — > increase in RMS error can be predicted • System estimation/prediction to model the pilot — Real-time parameter identification for changes in Kp and Kr in Hess simplified pursuit model — Model estimation with simple linear-in-parameter gradient estimator

ABSTRACT The experiments described here explored how pilots want available maneuver authority information transmitted and how this information affects pilots before and after an aircraft failure. The aircraft dynamic variables relative to flight performance were narrowed to energy management variables. A survey was conducted to determine what these variables should be. Survey results indicated that bank angle, vertical velocity, and airspeed were the preferred variables. Based on this, two displays were designed to inform the pilot of available maneuver envelope expressed as bank angle, vertical velocity, and airspeed. These displays were used in an experiment involving control surface failures. Results indicate the displayed limitations in bank angle, vertical velocity, and airspeed were helpful to the pilots during aircraft surface failures. However, the additional information did lead to a slight increase in workload, a small decrease in perceived aircraft flying qualities, and no effect on aircraft situation awareness.

ABSTRACT NASA aeronautics research has made decades of contributions to aviation. Both aircraft and air traffic management (ATM) systems in use today contain NASA-developed and NASAsponsored technologies that improve safety and efficiency. Recent innovations in robotics and autonomy for automobiles and unmanned systems point to a future with increased personal mobility and access to transportation, including aviation. Automation and autonomous operations will transform the way we move people and goods. Achieving this mobility will require safe, robust, reliable operations for both the vehicle and the airspace and challenges to this inevitable future are being addressed now in government labs, universities, and industry. These challenges are the focus of NASA Langley Research Center’s autonomy Incubator whose R&D portfolio includes mission planning, trajectory and path planning, object detection and avoidance, object classification, sensor fusion, controls, machine learning, computer vision, human-machine teaming, geo-containment, open architecture design and development, as well as the test and evaluation environment that will be critical to prove system reliability and support certification. Safe autonomous operations will be enabled via onboard sensing and perception systems in both data-rich and data-deprived environments. Applied autonomy will enable safety, efficiency and unprecedented mobility as people and goods take to the skies tomorrow just as we do on the road today.

Despite the contributions of automation to aviation safety and efficiency, the problems associated with technology-centered rather than human-centered automation are well known: decreased pilot situation awareness, deterioration of manual piloting skills, difficulties pilots experience when trying to jump into the loop when needed, and so forth. We present a prototype architecture for human-automation interaction that reverses their traditional roles: in our design, the automation "looks over the shoulder" of the pilot and jumps into the loop when needed rather than the other way around to prevent aircraft loss-of-control (LoC). The architecture exploits the LoC prevention algorithm proposed by Wilborn and Foster (2004). This quantitative definition uses a set of five two-dimensional envelopes relating to critical flight parameters that account for aircraft flight dynamics, aerodynamics, structural integrity, and flight control use. The LoC algorithm is used to both presen...

Advances in automation capability and reliability have changed the role of humans from operating and controlling processes to simply monitoring them for anomalies. However, humans are traditionally bad monitors of highly reliable systems over time. Thus, the human is assigned a task for which he or she is ill equipped. We believe that this has led to the dominance of human error in process control activities such as operating transportation systems (aircraft and trains), monitoring patient health in the medical industry, and controlling plant operations. Research has shown, though, that an automated monitor can assist humans in recognizing and dealing with failures. One possible solution to this predicament is to use a polar-star display that will show deviations from normal states based on parameters that are most indicative of mission health.