Masters Theses
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. In this thesis, Single Network Adaptive Critic (SNAC) based neural network structure is used to find the solution of a nonlinear TPBVP. Numerical simulations illustrating accurate state estimation and robustness of the Neuro-observer are presented. An analytical scheme using the State Dependant Riccati Equation (SDRE) is also employed to solve the nonlinear TPBVP. Illustrative simulation results are presented for the SORE based observer"--Abstract, page iii.
Advisor(s)
S. N. Balakrishnan
Committee Member(s)
Robert G. Landers
Donald C. Wunsch
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 pages
Note about bibliography
includes bibliographical references (pages 61-65)
Rights
© 2006 Venkat Phaneender Durbha, All rights reserved.
Document Type
Thesis - Restricted Access
File Type
text
Language
English
Subject Headings
Adaptive control systemsNeural networks (Computer science)Nonlinear control theoryRiccati equation
Thesis Number
T 9029
Print OCLC #
85764723
Recommended Citation
Durbha, Venkat, "Nonlinear optimal observer design using neural networks and state dependant Riccati equation" (2006). Masters Theses. 5923.
https://scholarsmine.mst.edu/masters_theses/5923
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