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DC Field | Value | Language |
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dc.contributor.author | Tomar, Dheerendra Singh | - |
dc.contributor.author | Gauci, Jason | - |
dc.contributor.author | Dingli, Alexiei | - |
dc.contributor.author | Muscat, Alan | - |
dc.contributor.author | Zammit-Mangion, David | - |
dc.date.accessioned | 2023-05-31T15:08:17Z | - |
dc.date.available | 2023-05-31T15:08:17Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Tomar, D. S., Gauci, J., Dingli, A., Muscat, A., & Mangion, D. Z. (2021, October). Automated aircraft stall recovery using reinforcement learning and supervised learning techniques. 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC), Texas. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/110281 | - |
dc.description.abstract | Despite the on-board automation and protection systems of modern commercial aircraft, aerodynamic stall events are still a possible occurrence. This paper proposes Machine Learning algorithms – based on Reinforcement Learning and Supervised Learning – to automatically recover an aircraft from two types of aerodynamic stall: unaccelerated wings level (1G) stall and a stall during a turn. The algorithms were tested by exposing them to 105 simulated stall scenarios with different initial conditions (including altitude, bank angle and wind speed) and an acceptable stall recovery was achieved in 85% of the test cases. Further work will focus on improving the performance of the algorithms such as by reducing the time to recover from a stall and decreasing the altitude loss. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Deep learning (Machine learning) | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Reinforcement learning | en_GB |
dc.subject | Airplanes -- Automatic control | en_GB |
dc.title | Automated aircraft stall recovery using reinforcement learning and supervised learning techniques | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 2021 IEEE/AIAA 40th Digital Avionics Systems Conference (DASC) | en_GB |
dc.bibliographicCitation.conferenceplace | San Antonio, United States. 03-07/10/2021. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
dc.identifier.doi | 10.1109/DASC52595.2021.9594316 | - |
Appears in Collections: | Scholarly works - InsAT |
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