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This paper presents a control architecture for aircraft, designed to enhance positioning accuracy and reliability during taxiing and runway operations. The system includes a data monitoring module that activates a position prediction mechanism using a Convolutional Neural Network (CNN) when GPS fails. Additionally, an intelligent control module based on transformer architecture manages aircraft movement. The architecture incorporates a Decision Transformer for reinforcement learning, refining control policies via supervised learning and Proximal Policy Optimization (PPO). An obstacle detection module, utilizing YOLOv7, ensures rapid and accurate object detection during taxiing. Experimental results validate the system’s efficiently across various weather and operational conditions, maintaining minimal deviation from the runway centerline and handling abnormal scenarios like engine or brake failures. The developed software and algorithmic support, tested in the Xplane-11 simulation environment, provide a robust solution aircraft control, ensuring safe and precise operations under diverse conditions.

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Development of Software and Algorithmic Support for the Navigation Function in Terms of Controlling the Executive Systems of the Small and Medium Capacity Aircraft at the Stage of Movement Along the Airfield

  • 会议论文

  • 发表日期：

    2026年

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  • N. I. Selvesyuk,
  • A. U. Checkin,
  • V. M. Novikov,
  • A. I. Proshunin,
  • M. E. Semenov,
  • A. M. Solovoyv

摘要

This paper presents a control architecture for aircraft, designed to enhance positioning accuracy and reliability during taxiing and runway operations. The system includes a data monitoring module that activates a position prediction mechanism using a Convolutional Neural Network (CNN) when GPS fails. Additionally, an intelligent control module based on transformer architecture manages aircraft movement. The architecture incorporates a Decision Transformer for reinforcement learning, refining control policies via supervised learning and Proximal Policy Optimization (PPO). An obstacle detection module, utilizing YOLOv7, ensures rapid and accurate object detection during taxiing. Experimental results validate the system’s efficiently across various weather and operational conditions, maintaining minimal deviation from the runway centerline and handling abnormal scenarios like engine or brake failures. The developed software and algorithmic support, tested in the Xplane-11 simulation environment, provide a robust solution aircraft control, ensuring safe and precise operations under diverse conditions.