Robust Control Feedback and Learning

Abstract

The research focused on broadening the class of solvable robust control problems and on developing a firm information theoretic foundation for incorporating the real-time effects of evolving experimental data. Robust control theory concerns the design of control systems capable of robustly maintaining performance to within prescribed tolerances in the face of large-but-bounded modeling uncertainties and nonlinearities. Significant advances were achieved in nonlinear robustness analysis for systems having repeated monotone nonlinearities and in reliable data-driven adaptive control synthesis techniques based on unfalsified control theory. The theory enables design of nonlinear feedback control systems that learn, discover and evolve in order to robustly compensate for battle damage, equipment failures and other changing circumstances.

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Document Details

Document Type
Technical Report
Publication Date
Nov 30, 2002
Accession Number
ADA399708

Entities

People

  • Michael G. Safonov

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Artificial Intelligence
  • Closed Loop Systems
  • Control Systems
  • Control Systems Engineering
  • Control Theory
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Engineers
  • Experimental Data
  • Feedback
  • Governments
  • Mathematical Models
  • New York
  • Signal Processing

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design