Title :
Neural identification based on sliding mode observer
Author :
Li, XiaoOu ; Yu, Wen
Author_Institution :
Dept. de Comput., CINVESTAV-IPN, Mexico City
Abstract :
In this paper, a new on-line neural identification method is presented. The identified nonlinear systems are partial-state measurement. Their inner states, parameters and structures are unknown. The design is based on the combination of a sliding mode observer and a neuro identifier. First, a sliding mode observer, which does not need any information of the nonlinear system, is applied to get the full states. Then a dynamic multilayer neural network is used to identify the whole nonlinear system. The main contributions of this paper are: (1) a new observer based identification algorithm is proposed; (2) a stable learning algorithm for the neuro identifier is given.
Keywords :
identification; learning (artificial intelligence); neural nets; nonlinear control systems; observers; variable structure systems; dynamic multilayer neural network; identified nonlinear system; online neural identification method; partial-state measurement; sliding mode observer; stable learning algorithm; Control systems; Control theory; Function approximation; Multi-layer neural network; Multilayer perceptrons; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Observers; Sliding mode control;
Conference_Titel :
Control Applications, 2007. CCA 2007. IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-0442-1
Electronic_ISBN :
978-1-4244-0443-8
DOI :
10.1109/CCA.2007.4389196
