Lessons learned from the developmental flight testing of the Terrain Awareness Warning System

The Ground Proximity W aming System (GPWS) currently fielded on the F/A-18A/B/C/D/E/F and AV-8B aircraft is a great safety-backup system that alerts the pilot of an impending Controlled Flight Into Terrain (CFIT) condition. However, it does have one major limitation: the reliance on the look-down radar altimeter, which results in little or no CFiT protection in rising terrain. The Terrain Awareness Warning System (TAWS) is the generational evolution of GPWS that provides the predictive, or look-ahead, capability sorely missing \u27rrom the current system. Utilizing aircraft positioning from the Global Positioning System (GPS) and an onboard Digital Terrain Elevation Data (DTED), TA WS computes recovery trajectories and presents a combination of aural and visual warnings when necessary to cue the pilot to avoid a CFiT condition. TA WS, without being solely reliant on the radar altimeter, has the ability to calculate and present appropriate warnings regardless of aircraft position or attitude. Ultimately, TA WS has to walk a fine line between providing timely warnings that allow the pilot to conduct maximum performance maneuvering during all mission roles, without the impedance of nuisance cues. At the heart of TA WS is a generic algorithm that can be tailored to specific aircraft performance and mission characteristics. This thesis examines all aspects of the flight test of TA WS: the history of GPWS and TA WS in aviation, the conundrum of how to plan a flight test of a terrain avoidance system in close proximity to the ground without endangering aircrew or aircraft, the use of simulation, additional safety precautions, results, lessons learned for program managers and test pilots, and future applications

Nonlinear stability and control study of highly maneuverable high performance aircraft

This project is intended to research and develop new nonlinear methodologies for the control and stability analysis of high-performance, high angle-of-attack aircraft such as HARV (F18). Past research (reported in our Phase 1, 2, and 3 progress reports) is summarized and more details of final Phase 3 research is provided. While research emphasis is on nonlinear control, other tasks such as associated model development, system identification, stability analysis, and simulation are performed in some detail as well. An overview of various models that were investigated for different purposes such as an approximate model reference for control adaptation, as well as another model for accurate rigid-body longitudinal motion is provided. Only a very cursory analysis was made relative to type 8 (flexible body dynamics). Standard nonlinear longitudinal airframe dynamics (type 7) with the available modified F18 stability derivatives, thrust vectoring, actuator dynamics, and control constraints are utilized for simulated flight evaluation of derived controller performance in all cases studied

Investigation of the Performance Characteristics of Re-Entry Vehicles

When a non-US spacecraft reenters the Earth\u27s atmosphere, having the ability to accurately determine its performance characteristics is a primary concern. This study investigated the atmospheric re-entry profiles of a maneuverable re-entry vehicle. The re-entry vehicle was modeled as a point mass with aerodynamic properties. Equations of motion were numerically integrated, giving the time histories of position, velocity and flight path angle. The algorithm is able to generate a complete and feasible entry trajectory of a approximately 25-minute flight time in about 5 to 10 seconds on a desktop computer, given the entry conditions and values of constraint parameters. This preliminary study shows the feasibility of identifying and further exploring the technical challenges involved in using a mathematical model to simulate the performance characteristics of the maneuvering re-entry vehicle

Path Planning Algorithm based on Arnold Cat Map for Surveillance UAVs

During their task accomplishment, autonomous unmanned aerial vehicles are facing more and more threats coming from both ground and air. In such adversarial environments, with no a priori information about the threats, a flying robot in charge with surveilling a specified 3D sector must perform its tasks by evolving on misleading and unpredictable trajectories to cope with enemy entities. In our view, the chaotic dynamics can be the cornerstone in designing unpredictable paths for such missions, even though this solution was not exploited until now by researchers in the 3D context. This paper addresses the flight path-planning issue for surveilling a given volume in adversarial conditions by proposing a proficient approach that uses the chaotic behaviour exhibited by the 3D Arnold’s cat map. By knowing the exact location of the volume under surveillance before take-off, the flying robot will generate the successive chaotic waypoints only with onboard resources, in an efficient manner. The method is validated by simulation in a realistic scenario using a detailed Simulink model for the X-4 Flyer quadcopter

Robust post-stall perching with a simple fixed-wing glider using LQR-Trees

Birds routinely execute post-stall maneuvers with a speed and precision far beyond the capabilities of our best aircraft control systems. One remarkable example is a bird exploiting post-stall pressure drag in order to rapidly decelerate to land on a perch. Stall is typically associated with a loss of control authority, and it is tempting to attribute this agility of birds to the intricate morphology of the wings and tail, to their precision sensing apparatus, or their ability to perform thrust vectoring. Here we ask whether an extremely simple fixed-wing glider (no propeller) with only a single actuator in the tail is capable of landing precisely on a perch from a large range of initial conditions. To answer this question, we focus on the design of the flight control system; building upon previous work which used linear feedback control design based on quadratic regulators (LQR), we develop nonlinear feedback control based on nonlinear model-predictive control and 'LQR-Trees'. Through simulation using a flat-plate model of the glider, we find that both nonlinear methods are capable of achieving an accurate bird-like perching maneuver from a large range of initial conditions; the 'LQR-Trees' algorithm is particularly useful due to its low computational burden at runtime and its inherent performance guarantees. With this in mind, we then implement the 'LQR-Trees' algorithm on real hardware and demonstrate a 95 percent perching success rate over 147 flights for a wide range of initial speeds. These results suggest that, at least in the absence of significant disturbances like wind gusts, complex wing morphology and sensing are not strictly required to achieve accurate and robust perching even in the post-stall flow regime.United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-10-1-0951)National Science Foundation (U.S.) (Award IIS-0915148

Aerospace medicine and biology: A continuing bibliography with indexes

This bibliography lists 187 reports, articles, and other documents introduced into the NASA scientific and technical information system in October, 1987