My Career at NASA

This viewgraph presentation reviews the work of the presenter at NASA Dryden Flight Research Center. He describes what he does, the projects that he has worked on and the background that led him to his position. The presentation has many pictures of aircraft in fligh

Autonomous Airborne Refueling Demonstration: Phase I Flight-Test Results

The first phase of the Autonomous Airborne Refueling Demonstration (AARD) project was completed on August 30, 2006. The goal of this 15-month effort was to develop and flight-test a system to demonstrate an autonomous refueling engagement using the Navy style hose-and-drogue air-to-air refueling method. The prime contractor for this Defense Advanced Research Projects Agency (DARPA) sponsored program was Sierra Nevada Corporation (SNC), Sparks, Nevada. The responsible flight-test organization was the National Aeronautics and Space Administration (NASA) Dryden Flight Research Center (DFRC), Edwards, California, which also provided the F/A-18 receiver airplane (McDonnell Douglas, now The Boeing Company, Chicago, Illinois). The B-707-300 tanker airplane (The Boeing Company) was contracted through Omega Aerial Refueling Services, Inc., Alexandria, Virginia, and the optical tracking system was contracted through OCTEC Ltd., Bracknell, Berkshire, United Kingdom. Nine research flights were flown, testing the functionality and performance of the system in a stepwise manner, culminating in the plug attempts on the final flight. Relative position keeping was found to be very stable and accurate. The receiver aircraft was capable of following the tanker aircraft through turns while maintaining its relative position. During the last flight, six capture attempts were made, two of which were successful. The four misses demonstrated excellent characteristics, the receiver retreating from the drogue in a controlled, safe, and predictable manner that precluded contact between the drogue and the receiver aircraft. The position of the receiver aircraft when engaged and in position for refueling was found to be 5.5 to 8.5 ft low of the ideal position. The controller inputs to the F/A-18 were found to be extremely small

Flight Test of the F/A-18 Active Aeroelastic Wing Airplane

Successful flight-testing of the Active Aeroelastic Wing airplane was completed in March 2005. This program, which started in 1996, was a joint activity sponsored by NASA, Air Force Research Laboratory, and industry contractors. The test program contained two flight test phases conducted in early 2003 and early 2005. During the first phase of flight test, aerodynamic models and load models of the wing control surfaces and wing structure were developed. Design teams built new research control laws for the Active Aeroelastic Wing airplane using these flight-validated models; and throughout the final phase of flight test, these new control laws were demonstrated. The control laws were designed to optimize strategies for moving the wing control surfaces to maximize roll rates in the transonic and supersonic flight regimes. Control surface hinge moments and wing loads were constrained to remain within hydraulic and load limits. This paper describes briefly the flight control system architecture as well as the design approach used by Active Aeroelastic Wing project engineers to develop flight control system gains. Additionally, this paper presents flight test techniques and comparison between flight test results and predictions

Development and Testing of Control Laws for the Active Aeroelastic Wing Program

The Active Aeroelastic Wing research program was a joint program between the U.S. Air Force Research Laboratory and NASA established to investigate the characteristics of an aeroelastic wing and the technique of using wing twist for roll control. The flight test program employed the use of an F/A-18 aircraft modified by reducing the wing torsional stiffness and adding a custom research flight control system. The research flight control system was optimized to maximize roll rate using only wing surfaces to twist the wing while simultaneously maintaining design load limits, stability margins, and handling qualities. NASA Dryden Flight Research Center developed control laws using the software design tool called CONDUIT, which employs a multi-objective function optimization to tune selected control system design parameters. Modifications were made to the Active Aeroelastic Wing implementation in this new software design tool to incorporate the NASA Dryden Flight Research Center nonlinear F/A-18 simulation for time history analysis. This paper describes the design process, including how the control law requirements were incorporated into constraints for the optimization of this specific software design tool. Predicted performance is also compared to results from flight

Modeling Aircraft Wing Loads from Flight Data Using Neural Networks

Neural networks were used to model wing bending-moment loads, torsion loads, and control surface hinge-moments of the Active Aeroelastic Wing (AAW) aircraft. Accurate loads models are required for the development of control laws designed to increase roll performance through wing twist while not exceeding load limits. Inputs to the model include aircraft rates, accelerations, and control surface positions. Neural networks were chosen to model aircraft loads because they can account for uncharacterized nonlinear effects while retaining the capability to generalize. The accuracy of the neural network models was improved by first developing linear loads models to use as starting points for network training. Neural networks were then trained with flight data for rolls, loaded reversals, wind-up-turns, and individual control surface doublets for load excitation. Generalization was improved by using gain weighting and early stopping. Results are presented for neural network loads models of four wing loads and four control surface hinge moments at Mach 0.90 and an altitude of 15,000 ft. An average model prediction error reduction of 18.6 percent was calculated for the neural network models when compared to the linear models. This paper documents the input data conditioning, input parameter selection, structure, training, and validation of the neural network models