Gpc-based deck motion estimation for autonomous ship deck landing of an unmanned aircraft

Rushabh Patel, Brian H. Le Floch, Eric N. Johnson, Jacob Crouse

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Landing an aircraft on a ship deck in high sea states can be challenging due to the risk for adverse interactions between the moving ship deck and aircraft components. When deck motion is significant, aircraft must sometimes wait for an extended duration until a calm period is detected to land safely. This paper presents a deck motion estimation (DME) algorithm that can decrease landing times. The DME algorithm runs in real-time allowing for fully autonomous ship deck landings without a human operator in the loop. The proposed solution uses generalized predictive control to predict future ship deck states based on prior observations. A landing is commanded when ship deck states are predicted to be within quiescent bounds for a pre-determined prediction horizon in which a safe landing could be completed. Monte-Carlo simulations utilizing ship state data show that the deck motion estimation can accurately predict quiescent landing periods. Further, flight test experiments have demonstrated the feasibility of implementing the system for an unmanned aircraft.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-8
Number of pages8
ISBN (Print)9781624106095
StatePublished - 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: Jan 11 2021Jan 15 2021

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period1/11/211/15/21

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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