A Comparison of Intelligent, Adaptive, and Nonlinear Flight Control Laws

Abstract

This paper compares in simulation six different nonlinear control laws for multi-axis control of a high performance aircraft. The control law approaches are fuzzy logic control, backstepping adaptive control, variable structure control, and indirect adaptive versions of Model Predictive Control and Dynamic Inversion. In addition, a more conventional scheduled dynamic inversion control law is used as a baseline. In some of the cases, a stochastic genetic algorithm was used to optimize fixed parameters during design. The control laws are demonstrated on a 6 Degree-of-Freedom simulation with nonlinear aerodynamic and engine models, actuator models with position and rate saturations, and turbulence. Simulation results include a variety of single and multiple axis maneuvers in normal operation and with failures or damage. The specific failure and damage cases that are examined include single and multiple lost surfaces, actuator hardovers, and an oscillating stabilator case. There are also substantial differences between the control law design and simulation models, which are used to demonstrate some robustness aspects of the different control laws.

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Document Details

Document Type
Technical Report
Publication Date
Jun 04, 1999
Accession Number
ADA368768

Entities

People

  • Marc L. Steinberg

Organizations

  • Naval Air Warfare Center

Tags

Communities of Interest

  • Air Platforms
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Actuators
  • Aeronautics
  • Aircrafts
  • Algorithms
  • Artificial Intelligence
  • Control Surfaces
  • Control Systems
  • Dynamic Pressure
  • Equations
  • Equations Of Motion
  • Flight Control Systems
  • Fuzzy Logic
  • Genetic Algorithms
  • Inversion
  • Model Predictive Control
  • Neural Networks
  • Simulations

Fields of Study

  • Physics

Readers

  • Aerospace Engineering
  • Robotics and Automation.

Technology Areas

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