Trajectory tracking via adaptive recurrent control with input saturation

Author

Sanchez, Edgar N. ; Ricalde, Luis J.

Author_Institution

CINVESTAV, Unidad Guadalajara, Mexico

Volume

1

fYear

2003

fDate

20-24 July 2003

Firstpage

359

Abstract

This paper deals with the adaptive tracking problem for nonlinear systems in presence of unknown parameters, unmodelled dynamics and input saturation. A high order recurrent neural network is used in order to identify the unknown system and a learning law is obtained using the Lyapunov methodology. Then stabilizing control law for the reference tracking error dynamics is developed using the Lyapunov methodology and the Sontag control law. Tracking error boundedness is established as a function of a design parameter.

Keywords

Lyapunov methods; adaptive control; control nonlinearities; neurocontrollers; nonlinear control systems; position control; recurrent neural nets; stability; Lyapunov methodology; Sontag control law; adaptive recurrent control; high order recurrent neural network; input saturation; nonlinear systems; reference tracking error dynamics; stability; tracking error boundedness; trajectory tracking; Adaptive control; Control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Recurrent neural networks; Stability; Trajectory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223372

Filename

1223372