Nonlinear and Structured Interpolation for Robust Control

Abstract

We have explored the problem of extending the linear H(infinity) theory to nonlinear systems, robust distributed parameter control, and the robust analysis and synthesis of controllers for systems in the presence of various classes of structured perturbations. We have employed operator theory, partial differential equations, optimization theory, and invariant theory to study the problem of utilizing visual information in a feedback loop and the closely related application of visual tracking. We have considered a combination of methods from robust control and computer vision for this purpose. Our approach to computer vision and image processing is based on certain novel curvature dependent evolution equations that may be employed for image enhancement, denoising, active contours, edge detection, morphology, shape recognition, shape-from-shading, stereo disparity, and optical flow. We have combined this with our longstanding work in robust control, and applying these ideas to certain benchmark problems in visual tracking.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1998
Accession Number
ADA358498

Entities

People

  • Allen R. Tannenbaum

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Change Detection
  • Computations
  • Computer Vision
  • Control Systems
  • Control Systems Engineering
  • Detection
  • Differential Equations
  • Equations
  • Functional Analysis
  • Geometry
  • Hilbert Space
  • Image Processing
  • Nonlinear Systems
  • Partial Differential Equations
  • Recognition
  • Target Recognition

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Computer Vision.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms