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    <title>OAR@UM Collection:</title>
    <link>https://www.um.edu.mt/library/oar/handle/123456789/125420</link>
    <description />
    <pubDate>Fri, 24 Apr 2026 07:24:07 GMT</pubDate>
    <dc:date>2026-04-24T07:24:07Z</dc:date>
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      <title>Path planning for emergency landings</title>
      <link>https://www.um.edu.mt/library/oar/handle/123456789/125732</link>
      <description>Title: Path planning for emergency landings
Abstract: In the event of critical failures that compromise an aircraft's ability to remain airborne, &#xD;
flight crews face significant challenges in planning and executing an emergency &#xD;
landing. Currently, there is minimal flight deck support to assist crews in navigating &#xD;
this high-workload and high-risk situation. To maximise the probability of a safe &#xD;
landing, the development of onboard crew support systems that assist the pilots in &#xD;
making correct and timely decisions under high workload is critical. Such systems &#xD;
must consider a range of factors including proximity to potential landing sites, &#xD;
remaining aircraft performance, weather conditions, and terrain profiles. This project &#xD;
focuses on planning the descent trajectory to a predefined location in emergency&#xD;
scenarios involving a complete loss of engine thrust, requiring the aircraft to glide to &#xD;
the landing site without engine power.&#xD;
The aim of this dissertation is to compare two distinct approaches to trajectory &#xD;
planning algorithms: the Particle Swarm Optimization (PSO) and Non-Linear &#xD;
Programming (NLP). Both optimisers utilise the Dubin’s path to generate the lateral &#xD;
path of the aircraft by adjusting the location of predefined waypoints. The optimisation &#xD;
process seeks to identify an optimal descent trajectory that satisfies several operational &#xD;
and performance-related constraints.&#xD;
Utilizing nonlinear and linear constraints for the NLP and penalty functions for the &#xD;
PSO, the algorithms managed to identify valid trajectories that allow the aircraft to &#xD;
arrive at the target location at the correct aircraft energy level required for subsequent &#xD;
landing. Notably, each algorithm demonstrated unique strengths, with the NLP often&#xD;
generating slightly preferable paths compared to those produced by the PSO.
Description: B.Eng. (Hons)(Melit.)</description>
      <pubDate>Mon, 01 Jan 2024 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://www.um.edu.mt/library/oar/handle/123456789/125732</guid>
      <dc:date>2024-01-01T00:00:00Z</dc:date>
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