Robust Adaptive Control Based on Neural Network for a Class of Uncertain Nonlinear Systems

Author

Li, Ningning ; Song, Su

Author_Institution

Coll. of Electron. Inf. & Control Eng., Beijing Univ. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

2388

Lastpage

2392

Abstract

It is a critical problem in the neural network adaptive control system to attenuate the influence of external disturbance or unmodeled dynamics and improve the robustness. In this paper, a novel robust adaptive control based on neural network for unknown nonlinear dynamical systems with bounded disturbances or unmodeled dynamics was proposed. It was realized by using adaptive forecasting and the recursive forgetting factor least square method, also the stability of system was guaranteed by a robust controller. The validity of this control strategy was demonstrated via simulation results

Keywords

adaptive control; least squares approximations; neurocontrollers; nonlinear control systems; robust control; uncertain systems; adaptive forecasting; least square method; neural network; recursive forgetting factor; robust adaptive control; uncertain nonlinear systems; Adaptive control; Educational institutions; Electronic mail; Least squares methods; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Programmable control; Robust control; Robust stability; adaptive forecasting; disturbance; neural network model reference adaptive control (NNMRAC); recursive least square method; robustness;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1712788

Filename

1712788