Doctoral Dissertations

Abstract

"In this dissertation, neural networks (NN) approximate unknown nonlinear functions in the system equations, unknown control inputs, and cost functions for two different classes of nonlinear discrete-time systems. Employing NN in closed-loop feedback systems requires that weight update algorithms be stable...Controllers are developed and applied to a nonlinear, discrete-time system of equations for a spark ignition engine model to reduce the cyclic dispersion of heat release"--Abstract, page iv.

Advisor(s)

Sarangapani, Jagannathan, 1965-

Committee Member(s)

Beetner, Daryl G.
Smith, Scott
Erickson, Kelvin T.
Drallmeier, J. A.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Sponsor(s)

National Science Foundation (U.S.)
United States. Department of Education

Publisher

University of Missouri--Rolla

Publication Date

Fall 2007

Journal article titles appearing in thesis/dissertation

  • Discrete-time neural network output feedback control of nonlinear discrete-time systems in non-strict form
  • Neural network controller development and implementation for spark ignition engines and high EGR levels
  • Neuro emission controller for minimizing cyclic dispersion in spark ignition engines with EGR levels
  • Output feedback controller for operation of spark ignition engines at lean conditions using neural networks
  • Reinforcement learning-based state-feedback control of nonaffine nonlinear discrete-time systems with application to engine spark timing control

Pagination

xiii, 222 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2007 Jonathan Blake Vance, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Feedback control systems
Neural networks (Computer science)
Nonlinear control theory
Reinforcement learning (Machine learning)

Thesis Number

T 9325

Print OCLC #

235572504

Electronic OCLC #

214086898

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