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Autonomous Landing and Go-around of Large Jets Under Severe Weather Conditions Using Artificial Neural Networks

Baomar, HAO; Bentley, PJ; (2017) Autonomous Landing and Go-around of Large Jets Under Severe Weather Conditions Using Artificial Neural Networks. In: Proceedings of the 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS). IEEE: Linköping, Sweden. Green open access

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Abstract

We introduce the Intelligent Autopilot System (IAS) which is capable of autonomous landing, and go-around of large jets such as airliners under severe weather conditions. The IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to autonomously handle flight uncertainties such as severe weather conditions, autonomous complete flights, and go-around. A robust approach to control the aircraft's bearing using Artificial Neural Networks is proposed. An Artificial Neural Network predicts the appropriate bearing to be followed given the drift from the path line to be intercepted. In addition, the capabilities of the Flight Manager of the IAS are extended to detect unsafe landing attempts, and generate a go-around flight course. Experiments show that the IAS can handle such flight skills and tasks effectively, and can even land aircraft under severe weather conditions that are beyond the maximum demonstrated landing of the aircraft model used in this work as reported by the manufacturer's operations limitations. The proposed IAS is a novel approach towards achieving full control autonomy of large jets using ANN models that match the skills and abilities of experienced human pilots.

Type: Proceedings paper
Title: Autonomous Landing and Go-around of Large Jets Under Severe Weather Conditions Using Artificial Neural Networks
Event: 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)
Location: Linköping, Sweden
Dates: 03 October 2017 - 05 October 2017
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/RED-UAS.2017.8101661
Publisher version: https://doi.org/10.1109/RED-UAS.2017.8101661
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Artificial neural networks, Aircraft, Wind, Training, Databases
UCL classification: UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1574504
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