Practical Control Algorithms for Nonlinear Stochastic Systems and Investigations of Nonlinear Filters.

Abstract

This report summarizes research in problems of stabilization and control of nonlinear stochastic systems observed by noisy measurement data, and the difficulties encountered in processing this noise-contaminated measurement data to obtain accurate estimates of the states of the system. Such problems are inherent in many Air Force systems applications. If it is possible to estimate the state of the system accurately, then well-known classical deterministic control techniques may often be used to give adequate system performance. This approach will greatly reduce the complexity of the control algorithms over that required by the optimal policy. On the other hand the use of nonlinear filtering techniques in place of the simpler linearized filters can greatly increase the accuracy of the state estimates and thereby improve system performance and alleviate divergence problems.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1980
Accession Number
ADA092199

Entities

People

  • Daniel L. Alspach
  • Harold W. Sorenson
  • Vivek S. Samant

Tags

Communities of Interest

  • Sensors
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Control Systems
  • Estimators
  • Filtration
  • Grids
  • Information Science
  • Kalman Filters
  • Linear Systems
  • Mathematical Filters
  • Parallel Computing
  • Probabilistic Models
  • Probability
  • Probability Density Functions
  • Probability Distributions
  • Random Variables
  • Statistics

Fields of Study

  • Engineering

Readers

  • Control Systems Engineering.
  • Image Processing and Computer Vision.
  • Operations Research