Dynamic neural network-based robust observers for second-order uncertain nonlinear systems

Author

Dinh, H. ; Kamalapurkar, R. ; Bhasin, S. ; Dixon, W.E.

Author_Institution

Dept. of Mech. & Aerosp. Eng., Univ. of Florida, Gainesville, FL, USA

fYear

2011

fDate

12-15 Dec. 2011

Firstpage

7543

Lastpage

7548

Abstract

A dynamic neural network (DNN) based robust observer for second-order uncertain nonlinear systems is developed. The observer structure consists of a DNN to estimate the system dynamics on-line, a dynamic filter to estimate the unmeasurable state and a sliding mode feedback term to account for modeling errors and exogenous disturbances. The observed states are proven to asymptotically converge to the system states though Lyapunov-based stability analysis.

Keywords

Lyapunov methods; asymptotic stability; feedback; filtering theory; neurocontrollers; nonlinear control systems; observers; robust control; uncertain systems; variable structure systems; Lyapunov-based stability analysis; asymptotic converge; dynamic filter; dynamic neural network-based robust observers; exogenous disturbances; modeling errors; second-order uncertain nonlinear systems; sliding mode feedback term; system dynamic estimation; system states; unmeasurable state estimation; Artificial neural networks; Estimation error; Nonlinear dynamical systems; Observers; Robustness; Stability analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on

Conference_Location

Orlando, FL

ISSN

0743-1546

Print_ISBN

978-1-61284-800-6

Electronic_ISBN

0743-1546

Type

conf

DOI

10.1109/CDC.2011.6160981

Filename

6160981