Title :
Inverse optimal nonlinear recurrent high order neural observer
Author :
Ricalde, Luis J. ; Sanchez, Edgar N.
Author_Institution :
CINVESTAV, Jalisco, Mexico
fDate :
31 July-4 Aug. 2005
Abstract :
This paper presents the design of an adaptive recurrent neural observer for nonlinear systems which model is assumed to be unknown. The neural observer is composed of a recurrent high order neural network which builds an online model of the unknown plant and a learning adaptation law for the neural network weights. This law is obtained by the Lyapunov methodology. The feedback law which guarantees stability of the estimation error is proved to be optimal with respect to a well defined cost functional.
Keywords :
Lyapunov methods; feedback; inverse problems; learning (artificial intelligence); nonlinear systems; observers; recurrent neural nets; Lyapunov method; adaptive recurrent neural observer; estimation error stability; feedback law; inverse optimal nonlinear recurrent high order neural observer; learning adaptation law; nonlinear systems; recurrent high order neural network; Control systems; Cost function; Estimation error; Neural networks; Nonlinear control systems; Nonlinear systems; Observers; Recurrent neural networks; Stability; State estimation;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1555857
