Doctoral Dissertations

Abstract

"Online trained neural networks have become popular in recent years in the design of robust and adaptive controllers for dynamic systems with uncertainties due to their universal function approximation capabilities. This research explores the application of online neural networks for the design of model following controllers and for dynamic reoptimization of a Single Network Adaptive Critic (SNAC) optimal controller. Model following controllers for a general class of nonlinear systems with unknown uncertainties in their modeling equations have been developed in this research. A desirable characteristic of the model following controller scheme elaborated in this work is that it can be used in conjunction with any known control design technique. This research also discusses a technique that dynamically re-optimizes a Single Network Adaptive Critic controller. The SNAC based optimal controller designed for the nominal plant model no more retains optimality in the presence of uncertainties/unmodeled dynamics that may creep up in the system equations during operation. This necessitates the application of online function approximating neural networks that can help in SNAC reoptimization. Neural network weight update rules for continuous and discrete time systems have been derived using Lyapunov theory that guarantees both the stability of error dynamics and boundedness of the neural network weights. Detailed proofs and numerical simulations of the online weight update rules on various engineering problems have been provided in this document"--Abstract, page iii.

Advisor(s)

Balakrishnan, S. N.

Committee Member(s)

Krishnamurthy, K.
Landers, Robert G.
Midha, A. (Ashok)
Wunsch, Donald C.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Mechanical Engineering

Sponsor(s)

National Science Foundation (U.S.)
University of Missouri--Rolla. Department of Mechanical and Aerospace Engineering

Publisher

University of Missouri--Rolla

Publication Date

Fall 2006

Pagination

ix, 114 pages

Note about bibliography

Includes bibliographical references (pages 109-113).

Rights

© 2006 Nishant Unnikrishnan, All rights reserved.

Document Type

Dissertation - Restricted Access

File Type

text

Language

English

Library of Congress Subject Headings

Adaptive control systems -- Mathematical models
Mathematical optimization
Neural networks (Computer science)
Nonlinear control theory
Nonlinear systems -- Mathematical models

Thesis Number

T 9018

Print OCLC #

85846448

Electronic OCLC #

994220462

Share

 
COinS