Adaptive Internal Model Control Research in Autonomous Landing Phase for a Fixed-wing UAV

Autonomous landing is a very complex phase of flight for unmanned aerial vehicle (UAV). Adaptive internal model control (AIMC) is proposed and applied on autonomous landing control system in this paper. Controllers are designed based on the decoupled and linearized models of a sample UAV. Estimation of process model is carried out to enhance system robustness, and filter parameter adjustment is proposed to achieve a good dynamic performance. Control effects are compared and analyzed between IMC and AIMC in different wind conditions which demonstrate that AIMC has better performances than IMC. At last, Monte Carlo simulations prove the system stability.

• Record URL:
  https://doi.org/10.1007/s13272-016-0216-1
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/18695582
• Supplemental Notes:
  • Copyright © 2017, Deutsches Zentrum für Luft- und Raumfahrt e.V.
• Authors:
  • Gao, Jiu-zhou
  • Jia, Hong-guang
• Publication Date: 2017-3

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: pp 45-51
• Serial:
  • CEAS Aeronautical Journal
  • Volume: 8
  • Issue Number: 1
  • Publisher: Springer-Verlag
  • ISSN: 1869-5582
  • EISSN: 1869-5590
  • Serial URL: http://link.springer.com/journal/13272

Subject/Index Terms

• TRT Terms: Air traffic control; Airplanes; Automatic landing systems; Drones; Monte Carlo method; Simulation
• Subject Areas: Aviation; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01627177
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 27 2017 9:38AM