A certifiable AI-based braking control framework for landing using scientific machine learning

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dc.contributor.authorUzun, Mevlut
dc.contributor.authorCelik, Ugurcan
dc.contributor.authorGuner, Guney
dc.contributor.authorOzdemir, Orhan
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2025-01-08T12:13:11Z
dc.date.available2025-01-08T12:13:11Z
dc.date.freetoread2025-01-08
dc.date.issued2024-09-29
dc.date.pubOnline2024-11-15
dc.description.abstractThis paper proposes an AI-based braking control system for aircraft during landing. Utilizing scientific machine learning, we train an agent to apply the most effective braking strategy under various landing conditions. This approach ensures physically consistent outputs by grounding the algorithm in the principles of landing physics. Our results demonstrate that the aircraft can successfully decelerate without skidding across all runway conditions and landing speeds. Additionally, the algorithm maintains performance and safety even when brake performance degradation and initial yaw angles are introduced. This robustness is crucial for the certification of AI in safety-critical systems, as the proposed framework provides a reliable and effective solution.
dc.description.conferencename2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC)
dc.description.sponsorshipInnovate UK
dc.description.sponsorshipThis work is part of the LANDOne project, funded by Innovate UK, a part of UK Research and Innovation, under grant number 10002411. DAS – No (Gemma copied in as UKRI funded)
dc.identifier.citationUzun M, Celik U, Guner G, et al., (2024) A certifiable AI-based braking control framework for landing using scientific machine learning. In: 2024 AIAA DATC/IEEE 43rd Digital Avionics Systems Conference (DASC), 29 September 2024 - 3 October 2024, San Diego, CA, USA
dc.identifier.eissn2155-7209
dc.identifier.elementsID559315
dc.identifier.issn2155-7195
dc.identifier.urihttps://doi.org/10.1109/dasc62030.2024.10749078
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23331
dc.language.isoen
dc.publisherIEEE
dc.publisher.urihttps://ieeexplore.ieee.org/document/10749078
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4007 Control Engineering, Mechatronics and Robotics
dc.subject40 Engineering
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectMachine Learning and Artificial Intelligence
dc.titleA certifiable AI-based braking control framework for landing using scientific machine learning
dc.typeConference paper
dcterms.coverageSan Deigo, CA. USA
dcterms.dateAccepted2024-04-04
dcterms.temporal.endDate3 Oct 2024
dcterms.temporal.startDate29 Sep 2024

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