Masters Theses
Keywords and Phrases
Exhaust gas recirculation (EGR)
Abstract
"A spark ignition (SI) engine can be described by non-strict feedback nonlinear discrete-time system with the output dependent upon on the states in a nonlinear manner. The controller developed in this thesis utilizes the inherent universal approximation property of neural networks (NN) to simplify the design process and solve the non-causality problem inherent with traditional designs. It also exploits a long-term performance index called the strategic utility function to minimize and assist in updating of the NN weights; therefore, an optimal controller can be realized. Finally, through Lyapunov equations, the controller guarantees stability"--Abstract, page iv.
Advisor(s)
Sarangapani, Jagannathan, 1965-
Committee Member(s)
Smith, Scott C.
Drallmeier, J. A.
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Computer Engineering
Publisher
University of Missouri--Rolla
Publication Date
Spring 2007
Pagination
xi, 93 pages
Rights
© 2007 Peter Shih, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Automobiles -- Motors -- Computer control systems
Automobiles -- Motors -- Exhaust gas
Neural networks (Computer science)
Reinforcement learning (Machine learning)
Thesis Number
T 9155
Print OCLC #
173691079
Electronic OCLC #
128261909
Link to Catalog Record
Recommended Citation
Shih, Peter, "Reinforcement-learning based output-feedback controller for nonlinear discrete-time system with application to spark ignition engines operating lean and EGR" (2007). Masters Theses. 4550.
https://scholarsmine.mst.edu/masters_theses/4550
