Adaptive output feedback control of nonlinear systems using dynamic neural networks

Author

Niu, Yugang ; Wang, Xingyu ; Hu, Chen

Author_Institution

Sch. of Inf., East China Univ. of Sci. & Technol., Shanghai, China

Volume

1

fYear

2002

fDate

2002

Firstpage

1

Abstract

In this paper, a dynamic-neural-networks-based adaptive output feedback controller for a class of unknown nonlinear systems is developed under the constraint that only the system output is available for measurement. A model-free state observer is utilized to estimate the system states. Moreover, the effect of network modeling error is also discussed. By means of a Lyapunov method and a matrix Riccati equation, it has been shown that the output feedback control law and weight update laws provide robust stability for the closed-loop system, and guarantee that all signals involved are bounded and the system output tracking error is uniformly ultimately bounded.

Keywords

Lyapunov methods; Riccati equations; adaptive control; closed loop systems; feedback; matrix algebra; neurocontrollers; nonlinear control systems; observers; robust control; uncertain systems; Lyapunov method; UUB output tracking error; adaptive output feedback control; closed-loop system; dynamic neural networks; matrix Ricatti equation; model-free state observer; network modeling error; output feedback control law; robust stability; system state estimation; uniformly ultimately bounded output tracking error; unknown nonlinear systems; weight update laws; Adaptive control; Control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Observers; Output feedback; Programmable control; Riccati equations;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on

Print_ISBN

0-7803-7268-9

Type

conf

DOI

10.1109/WCICA.2002.1022057

Filename

1022057