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Reinforcement-Learning-Based Adaptive Optimal Flight Control with Output Feedback and Input Constraints

Bo Sun, Erik-Jan van Kampen

JOURNAL OF GUIDANCE CONTROL AND DYNAMICS(2021)

引用 11|浏览3
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No AccessEngineering NotesReinforcement-Learning-Based Adaptive Optimal Flight Control with Output Feedback and Input ConstraintsBo Sun and Erik-Jan van KampenBo Sun https://orcid.org/0000-0002-5229-8545Delft University of Technology, 2629 HS Delft, The Netherlands and Erik-Jan van Kampen https://orcid.org/0000-0002-5593-4471Delft University of Technology, 2629 HS Delft, The NetherlandsPublished Online:8 Jun 2021https://doi.org/10.2514/1.G005715SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Sun B. and van Kampen E.-J., "Incremental Model-Based Global Dual Heuristic Programming with Explicit Analytical Calculations Applied to Flight Control," Engineering Applications of Artificial Intelligence, Vol. 89, March 2020, Paper 103425. https://doi.org/10.1016/j.engappai.2019.103425 Google Scholar[2] Junell J., Mannucci T., Zhou Y. and van Kampen E.-J., "Self-Tuning Gains of a Quadrotor Using a Simple Model for Policy Gradient Reinforcement Learning," AIAA Guidance, Navigation, and Control Conference, AIAA Paper 2016-1387, 2016. https://doi.org/10.2514/6.2016-1387 LinkGoogle Scholar[3] Ferrari S. and Stengel R. 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Google Scholar Previous article Next article FiguresReferencesRelatedDetailsCited byObserver-based optimal control method combination with event-triggered strategy for hypersonic morphing vehicleAerospace Science and Technology, Vol. 136A Reinforcement Learning Method to Trajectory Design for Manned Lunar Mission via Reshaping Rewards31 January 2023Vision-Based Nonlinear Incremental Control for a Morphing Wing With Mechanical ImperfectionsIEEE Transactions on Aerospace and Electronic Systems, Vol. 58, No. 6Test and Evaluation of Reinforcement Learning via Robustness Testing and Explainable AI for High-Speed Aerospace VehiclesEvent-triggered intelligent critic control with input constraints applied to a nonlinear aeroelastic systemAerospace Science and Technology, Vol. 120Event-triggered constrained control using explainable global dual heuristic programming for nonlinear discrete-time systemsNeurocomputing, Vol. 468 What's Popular Volume 44, Number 9September 2021 CrossmarkInformationCopyright © 2021 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-3884 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsArtificial IntelligenceArtificial Neural NetworkComputing SystemComputing and InformaticsComputing, Information, and CommunicationControl TheoryData ScienceFeedback ControlGuidance, Navigation, and Control SystemsMachine LearningOptimal Control Theory KeywordsFull State FeedbackReinforcement LearningFlight ControlElevator DeflectionTracking ControlANNBang Bang ControlNonlinear SystemsAerospace SystemMonte Carlo SimulationAcknowledgmentThe authors would like to thank the Chinese Scholarship Council for financial support for B. Sun with the project reference number of 201806290007.PDF Received10 October 2020Accepted1 May 2021Published online8 June 2021
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Reinforcement Learning,Optimal Control,Adaptive Dynamic Programming,Psychological Adjustment
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