Flight Management of an E-VTOL Fleet Using ANFIS Network

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Jahanbakhsh, Amir Mohammad | 2022

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 54875 (45)
University: Sharif University of Technology
Department: Aerospace Engineering
Advisor(s): Malaek, Mohammad Bagher
Abstract:
Much research has been done in the field of queuing or forecasting aircraft routes. These methods have been implemented mainly for commercial aircraft, and little research has been done on electric birds. In this research, we intend to extend these methods to the field of electric birds and create a database based on the existing records of successful flights that electric birds have had in the landing phase. Next, design an intelligent learning system that adjusts its parameters and has the ability to predict the route for birds planning to land at an airport. This intelligent system can manage the birds that are going to land with high computational speed and keep it up to date by adding any successful flight to its database. Such an intelligent system can prevent accidents from occurring during this phase of the flight and reduce the training that pilots need to learn from this type of flying equipment
Keywords:
Sequencing ; Adaptive Neuro-Fuzzy Inference System (ANFIS) ; Path Planning ; Electric Vertical Takeoff and Landing Aircraft (EVTOL) ; Aircraft Routing ; Electric Aircraft ; Min-Max Optimization

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