A Set-Based Methodology for White Noise Modeling

Abstract

This paper provides a new framework for analyzing white noise disturbances in linear systems: rather than the usual stochastic approach, noise signals are described as elements in sets and their effect is analyzed from a worst-case perspective. The paper studies how these sets must be chosen in order to have adequate properties for system response in the worst-case, statistics consistent with the stochastic point of view, and simple descriptions that allow for tractable worst-case analysis. The methodology is demonstrated by considering its implications in two problems: rejection of white noise signals in the presence of system uncertainty, and worst-case system identification.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1995
Accession Number
ADA462253

Entities

People

  • Fernando Paganini

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Computational Science
  • Data Science
  • Distribution Functions
  • Electrical Engineering
  • Ergodic Processes
  • Frequency
  • Frequency Domain
  • Functional Analysis
  • Information Science
  • Noise
  • Power Spectra
  • Probabilistic Models
  • Probability
  • Random Variables
  • Statistical Algorithms
  • Stochastic Processes
  • White Noise

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.