Stochastic Control and Nonlinear Estimation

Abstract

In stochastic control, a major focus of this research was numerical methods for finding approximately optimal control laws. Dynamic programming and Monte Carlo optimization algorithms were followed. Both probabilistic methods, based on weak convergence ideas, and analytical methods were used to prove convergence of algorithms. The latter were based on viscosity solution methods for nonlinear partial differential equations. In nonlinear estimation, low dimensional approximate nonlinear filters were found for cases when a piecewise one-to-one function of a system state plus low intensity observation noise was observed.

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Document Details

Document Type
Technical Report
Publication Date
Jul 07, 1992
Accession Number
ADA253543

Entities

People

  • Harold J. Kushner
  • Wendell Fleming

Organizations

  • Brown University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Asymptotic Series
  • Calculus Of Variations
  • Convergence
  • Differential Equations
  • Dynamic Programming
  • Equations
  • Kalman Filters
  • Markov Processes
  • Observation
  • Optimization
  • Partial Differential Equations
  • Probability
  • Stochastic Control
  • Viscosity
  • Weak Convergence

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Statistical inference.