Single Controller for Multiple Aircraft by Deep Reinforcement Learning
Fatih Ahmet SARIGUL, Ismail BAYEZIT
Abstract. This study introduces a DRL-based controller for multiple fixed-wing aircraft without model-specific tuning. The proposed system is trained using Proximal Policy Optimization (PPO) and is enhanced by algorithmic strategies such as multi-model training and kinematics-based reward shaping to ensure generalization across diverse platforms. It is compared with a conventional method to see if the generalized controller maintains high control performance at the level of an aircraft-specific design. This approach presents an adaptable alternative to conventional control design, offering resilience to configuration changes, and providing reduced dependency on the model.
Keywords
AI-Based Control, Generalized Flight Control, Deep Reinforcement Learning (DRL), Fixed-Wing Aircraft Control
Published online 7/20/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Fatih Ahmet SARIGUL, Ismail BAYEZIT, Single Controller for Multiple Aircraft by Deep Reinforcement Learning, Materials Research Proceedings, Vol. 69, pp 15-20, 2026
DOI: https://doi.org/10.21741/9781644904251-3
The article was published as article 3 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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