Adaptive, Integrated Guidance and Control Design for Line-of-Sight Based Formation Flight

Abstract

This paper presents an integrated guidance and control design for formation flight using a combination of adaptive output feedback and backstepping techniques without an underlying time-scale separation assumption. We formulate the problem as an adaptive output feedback control problem for a line-of-sight (LOS) based formation flight configuration of a leader and a follower aircraft. The design objective is to regulate range and two bearing angle rates while maintaining turn coordination. Adaptive neural networks are trained online with available measurements to compensate for unmodeled nonlinearities in the design process. These include uncertainties due to unknown leader aircraft acceleration, and the modeling error due to parametric uncertainties in the aircraft aerodynamic derivatives. One benefit of this approach is that the guidance and flight control design process is integrated. Simulation results using a nonlinear 6DOF simulation model are presented to illustrate the efficacy of the approach by comparing the performance with a time-scale separation based design.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 2006
Accession Number
ADA499368

Entities

People

  • Anthony J. Calise
  • Byoung S. Kim
  • Ramachandra J. Sattigeri

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Air Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Automatic Pilots
  • Closed Loop Systems
  • Computational Science
  • Computer Vision
  • Control Systems
  • Coordinate Systems
  • Euler Angles
  • Filters
  • Flight
  • Formation Flight
  • Guidance
  • Image Processing
  • Kalman Filters
  • Line Of Sight
  • Neural Networks
  • Unmanned Aerial Vehicles

Fields of Study

  • Physics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms