DEEP LEARNING BASED FLY-OVER WAYPOINTS CONTROL SYSTEM FOR BUSINESS JET AIRCRAFT
In order to reach the intended destination, Area Navigation (RNAV) requires pilot to use fly-by and fly-over waypoints method. System that developed in this thesis is focusing on implementing fly-over waypoints method. PID controller strategies are commonly found in building this control syste...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/69001 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | In order to reach the intended destination, Area Navigation (RNAV) requires pilot to
use fly-by and fly-over waypoints method. System that developed in this thesis is
focusing on implementing fly-over waypoints method. PID controller strategies are
commonly found in building this control system. However, by seeing the advantages
offered by Deep Learning (DL) which can overcome problems that occurs in PID
strategy. Hence, this thesis aims to develop DL-based fly over waypoints control
system for Cirrus Vision SF50 aircraft and study the relationship between learning data
characteristic with the result of control performance. Reconstruction of flight mission
data through flight simulator that integrated with PID-based fly-over waypoints method
is firstly done before creating DL model. The result obtained through this research are
several DL-based fly-over control systems are able to provide a better balance of
minimum distance to waypoints and cross track distances than the PID controller
method. It was also found that the control characteristic of the DL model is closely
related to the characteristic of the data used to train the model.
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