Deep Reinforcement Learning Automatic Landing Control of Fixed-Wing Aircraft Using Deep Deterministic Policy Gradient

Chi Tang, Ying Chih Lai

研究成果: Conference contribution

摘要

Landing phase remains to be one of the most crucial and difficult tasks to achieve among the flight envelope of an aircraft. The proof-of-concept controller in this research implemented the use of DDPG (Deep Deterministic Policy Gradient), a DRL (Deep Reinforcement Learning) approach in attempt to find policies of aircraft landings given designed requirements, or rewards. This research provided new methods in reward shaping, or reward engineering used during training and the investigation of the effects of hyperparameters and different network topologies of Neural Networks in training of aircraft landing control. The results of outer loop control and direct control in this research using DDPG on aircraft landing, with comparisons of numerous baseline and Neural Network approaches, proves the ability and potential of such DRL method, and is validated in numerous wind disturbance conditions, which demonstrated the robustness of DDPG agent. It is also found that besides the ability of DDPG agents to develop control policies for aircraft landings, such method provides insights of the controls and states of aircraft during landing, enabling guidelines of the flight characteristics of the aircraft in landing for pilots or design of controllers.

原文English
主出版物標題2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面1-9
頁數9
ISBN(電子)9781728142777
DOIs
出版狀態Published - 2020 九月
事件2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020 - Athens, Greece
持續時間: 2020 九月 12020 九月 4

出版系列

名字2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020

Conference

Conference2020 International Conference on Unmanned Aircraft Systems, ICUAS 2020
國家Greece
城市Athens
期間20-09-0120-09-04

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Control and Optimization

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