Uzun, MevlutCelik, UgurcanGuner, GuneyOzdemir, OrhanInalhan, Gokhan2025-01-082025-01-082024-09-29Uzun 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, USA2155-7195https://doi.org/10.1109/dasc62030.2024.10749078https://dspace.lib.cranfield.ac.uk/handle/1826/23331This 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/46 Information and Computing Sciences4007 Control Engineering, Mechatronics and Robotics40 EngineeringNetworking and Information Technology R&D (NITRD)Machine Learning and Artificial IntelligenceConference paper2155-7209559315