Masters Theses

Title

Nonlinear optimal observer design using neural networks and state dependant Riccati equation

Alternative Title

Nonlinear optimal observer design using neural networks and state dependent

Author

Venkat Durbha

Abstract

"The difficulty in measuring all the required states of a system necessitates the use of observers/estimators. The use of Extended Kalman Observer/Filter (EKO/EKF) for providing optimal state estimates for nonlinear system is well documented in literature. However the linearization assumption inherent in EKO/EKF implementation hinders its estimation characteristics. In this work a new optimal method for estimating states of a nonlinear system is presented. The observer design is posed as an optimal output tracking problem by defining an appropriate cost function. The correction to the observer states is shown to be function of the solution of the Two Point Boundary Value Problem (TPBVP) resulting from minimizing the cost function"--Abstract, leaf iii.

Advisor(s)

Balakrishnan, S. N.

Committee Member(s)

Landers, Robert G.
Wunsch, Donald C.

Department(s)

Mechanical and Aerospace Engineering

Degree Name

M.S. in Mechanical Engineering

Sponsor(s)

National Science Foundation (U.S.)

Publisher

University of Missouri--Rolla

Publication Date

Summer 2006

Pagination

viii, 66 leaves

Rights

© 2006 Venkat Phaneender Durbha, All rights reserved.

Document Type

Thesis - Citation

File Type

text

Language

English

Library of Congress Subject Headings

Adaptive control systems
Neural networks (Computer science)
Nonlinear control theory
Riccati equation

Thesis Number

T 9029

Print OCLC #

85764723

Link to Catalog Record

Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.

http://laurel.lso.missouri.edu/record=b5791890~S5

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