Nonlinear optimal observer design using neural networks and state dependant Riccati equation
Nonlinear optimal observer design using neural networks and state dependent
"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.
Balakrishnan, S. N.
Landers, Robert G.
Wunsch, Donald C.
Mechanical and Aerospace Engineering
M.S. in Mechanical Engineering
National Science Foundation (U.S.)
University of Missouri--Rolla
viii, 66 leaves
© 2006 Venkat Phaneender Durbha, All rights reserved.
Thesis - Citation
Adaptive control systems
Neural networks (Computer science)
Nonlinear control theory
Print OCLC #
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
Durbha, Venkat, "Nonlinear optimal observer design using neural networks and state dependant Riccati equation" (2006). Masters Theses. 5923.
Share My Thesis If you are the author of this work and would like to grant permission to make it openly accessible to all, please click the button above.