Real-Time Terminal Area Trajectory Planning for Runway Independent Aircraft

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dc.contributor.advisorAtkins, Ella Men_US
dc.contributor.authorXue, Minen_US
dc.contributor.departmentAerospace Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2006-02-04T08:26:11Z
dc.date.available2006-02-04T08:26:11Z
dc.date.issued2006-01-24en_US
dc.description.abstractThe 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.en_US
dc.format.extent12030469 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/3323
dc.language.isoen_US
dc.subject.pqcontrolledEngineering, Aerospaceen_US
dc.subject.pqcontrolledArtificial Intelligenceen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pquncontrolledTrajectory planningen_US
dc.subject.pquncontrolledpath planningen_US
dc.subject.pquncontrolledsimulated annealingen_US
dc.subject.pquncontrolledgenetic algorithmen_US
dc.subject.pquncontrolledair traffic managementen_US
dc.subject.pquncontrollednoise abatement procedureen_US
dc.titleReal-Time Terminal Area Trajectory Planning for Runway Independent Aircraften_US
dc.typeDissertationen_US

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