"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.
Sarangapani, Jagannathan, 1965-
Beetner, Daryl G.
Erickson, Kelvin T.
Drallmeier, J. A.
Electrical and Computer Engineering
Ph. D. in Electrical Engineering
National Science Foundation (U.S.)
United States. Department of Education
University of Missouri--Rolla
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
xiii, 222 pages
© 2007 Jonathan Blake Vance, All rights reserved.
Dissertation - Open Access
Library of Congress Subject Headings
Feedback control systems
Neural networks (Computer science)
Nonlinear control theory
Reinforcement learning (Machine learning)
Print OCLC #
Electronic OCLC #
Link to Catalog Recordhttp://laurel.lso.missouri.edu/record=b6430505~S5
Vance, Jonathan B., "Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control" (2007). Doctoral Dissertations. 2219.