Robust Control Design for Flight Control

Abstract

This project studies the application of new and advanced methods of control law synthesis for robust stabilization with respect to a combination of both unstructured model uncertainty (arising from neglected parasitic dynamics) and structured model uncertainty (arising from parametric variations which occur as flight conditions change). Efforts have focused on the characterization of a class of nonlinear models for longitudinal dynamics of aircraft in level flight subject to changes in static stability. Such 'relaxed stability' aircraft configurations are currently at issue in a wide variety of advanced designs including commercial transport and high performance aircraft. The control design approach employs H-infinity synthesis methods for optimal robust stabilization for the unstructured model uncertainty, and the requirement of worst case (i.e., robust) design for parametric uncertainty is included using a minimax optimization criteria. This study has focused on a class of flight control models with physically based parametric uncertainty. For these models, the solution of the minimax or worst case design is achieved by a straightforward procedure which can be readily combined with the requirements for robustness to parasitic dynamics using the closed form solution of the optical robust stabilization method.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1989
Accession Number
ADA211957

Entities

People

  • Harold G. Kwatny
  • Keith Glover
  • William H. Bennett

Tags

Communities of Interest

  • Cyber
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Aircrafts
  • Airframes
  • Closed Loop Systems
  • Commercial Aircraft
  • Computational Science
  • Control Systems
  • Engineering
  • Feedback
  • Frequency
  • Level Flight
  • Linear Systems
  • Multiple Input Multiple Output
  • Nonlinear Dynamics
  • Nonlinear Systems
  • Transport Aircraft
  • Vehicles

Readers

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