Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control
"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
Note about bibliography
Includes bibliographical references.
© 2007 Jonathan Blake Vance, All rights reserved.
Dissertation - Open Access
Feedback control systems
Neural networks (Computer science)
Nonlinear control theory
Reinforcement learning (Machine learning)
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Vance, Jonathan B., "Neural network control of nonstrict feedback and nonaffine nonlinear discrete-time systems with application to engine control" (2007). Doctoral Dissertations. 2219.