Real-Time Terminal Area Trajectory Planning for
Runway Independent Aircraft
Real-Time Terminal Area Trajectory Planning for
Runway Independent Aircraft
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Date
2006-01-24
Authors
Xue, Min
Advisor
Atkins, Ella M
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DRUM DOI
Abstract
The increasing demand for commercial air transportation results in delays due
to traffic queues that form bottlenecks along final approach and departure corridors.
In urban areas, it is often infeasible to build new runways, and regardless of automation
upgrades traffic must remain separated to avoid the wakes of previous aircraft.
Vertical or short takeoff and landing aircraft as Runway Independent Aircraft (RIA)
can increase passenger throughput at major urban airports via the use of vertiports
or stub runways. The concept of simultaneous non-interfering (SNI) operations has
been proposed to reduce traffic delays by creating approach and departure corridors
that do not intersect existing fixed-wing routes. However, SNI trajectories open
new routes that may overfly noise-sensitive areas, and RIA may generate more noise
than traditional jet aircraft, particularly on approach.
In this dissertation, we develop efficient SNI noise abatement procedures applicable
to RIA. First, we introduce a methodology based on modified approximated
cell-decomposition and Dijkstra's search algorithm to optimize longitudinal plane
(2-D) RIA trajectories over a cost function that minimizes noise, time, and fuel
use. Then, we extend the trajectory optimization model to 3-D with a k-ary tree
as the discrete search space. We incorporate geography information system (GIS)
data, specifically population, into our objective function, and focus on a practical
case study: the design of SNI RIA approach procedures to Baltimore-Washington
International airport. Because solutions were represented as trim state sequences,
we incorporated smooth transition between segments to enable more realistic cost
estimates.
Due to the significant computational complexity, we investigated alternative
more efficient optimization techniques applicable to our nonlinear, non-convex, heavily
constrained, and discontinuous objective function. Comparing genetic algorithm
(GA) and adaptive simulated annealing (ASA) with our original Dijkstra's algorithm,
ASA is identified as the most efficient algorithm for terminal area trajectory
optimization. The effects of design parameter discretization are analyzed, with results
indicating a SNI procedure with 3-4 segments effectively balances simplicity
with cost minimization. Finally, pilot control commands were implemented and generated
via optimization-base inverse simulation to validate execution of the optimal
approach trajectories.