Although today's aircraft provide a safe and reliable form of transportation,

in this era of stringent safety requirements and increased hostile threat at home

and abroad, accidents do occur. In the event of an emergency, rapid and precise

action is required to avoid the loss of aircraft, crew, and any potential passengers.

Some of the most difficult emergencies to manage are those that alter or reduce

aircraft performance. When such failures occur, aircraft control can become more

complex, requiring in some cases the pilot to re-learn how to fly. Moreover, once

these new dynamics are learned, the pilot must effectively utilize them to ensure

a safe landing. Providing this capability has been the goal of many researchers

as they improve aircraft avionics and mechanical systems, although work done

to develop emergency flight planners for reduced performance aircraft has been

lacking.

This thesis presents a general method of autonomously generating emergency

flight trajectories for post-failure aircraft connecting the aircraft with a desired landing site. This emergency flight planner utilizes a simplified aircraft kinematic

model allowing rapid computation of aircraft configuration changes from a

sequence of trimmed, i.e., non-accelerating, flight conditions. The complete set

of attainable trimmed flight conditions yields an accurate approximation of the

post-failure flight envelope, guaranteeing the production of feasible flight plans.

To facilitate accurate results, the feasibility and configuration impact of the dynamic

transitions between these trim states must also be addressed. The flight

planner uses a combination of discrete search and local continuous optimization

techniques to piece together from compiled trim and transition databases, finding

the necessary flight segment durations that produce the desired feasible flight

trajectory to a known desired landing site. A case study focusing on lateral actuator

(aileron and rudder) jams of an F-16 aircraft is used to demonstrate flight

planner performance.