Robust Control, Feedback and Learning: Data-Driven Methods

Abstract

The research effort supported under this grant ended 31 July 2004. A highlight of progress during the final fiscal year was a clear theoretical explanation of and solution to. the model mismatch stability problem generally associated with state-of-the-art adaptive control design methods. The source of these model-mismatch instability problems was traced to the implicit use of absolute-error cost functions and/or use of assumption-driven cost functions. These cost-functions were shown via counter-example to he capable of giving preference to destabilizing control laws in some cases involving model mismatch. Having identified the model- mismatch problem, a fix was also identified, which is to employ a data-driven input-output-gain related cost function for adaptive control-law selection. This progress was enabled by theory that explains the synthesis of adaptive control processes in terms of control law unfalsification.

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Document Details

Document Type
Technical Report
Publication Date
Jul 31, 2004
Accession Number
ADA427715

Entities

People

  • Michael G. Safonov

Organizations

  • University of Southern California

Tags

Communities of Interest

  • Energy and Power Technologies
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Adaptive Control Systems
  • Adaptive Systems
  • Closed Loop Systems
  • Communication Networks
  • Control Systems
  • Control Systems Engineering
  • Electrical Engineering
  • Feedback
  • Flight Control Systems
  • Industrial Control Systems
  • Instability
  • Learning
  • Mathematical Models
  • Models
  • Networks
  • Signal Processing
  • Wireless Networks

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design
  • Technical Research and Report Writing.