Neural Network Based Adaptive Control of Uncertain and Unknown Nonlinear Systems

Abstract

The objectives of this research effort were to exploit recent advances in neural network (NN) based adaptive control, with the goal of being able to treat a very general class of nonlinear system, for which the dynamics are not only uncertain, but may in fact be unknown except for minimal structural information, such as the relative degree of the regulated output variables. We were particularly interested in designing adaptive control systems that are robust with respect to both parametric uncertainty and unmodeled dynamics. Extensions to decentralized control were also of interest. In addition, we placed a high priority on transition opportunities in aircraft flight control, control of flows, control of flexible space structures, and control of aeroelastic wings.

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Document Details

Document Type
Technical Report
Publication Date
Mar 31, 2004
Accession Number
ADA425419

Entities

People

  • Anthony J. Calise

Organizations

  • Georgia Tech

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Adaptive Control Systems
  • Aircrafts
  • Airframes
  • Artificial Intelligence
  • Automatic Pilots
  • Collision Avoidance
  • Control Systems
  • Dynamics
  • Governments
  • Guidance
  • Launch Vehicles
  • Multiple Input Multiple Output
  • Navigation
  • Neural Networks
  • Nonlinear Systems
  • Uncertainty
  • Vehicles

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Spacecraft Maneuvers