Adaptive NN control of strict-feedback systems using ISS-modular approach

Author

Ren, Beibei ; Ge, Shuzhi Sam ; Lee, Tong Heng

Author_Institution

Nat. Univ. of Singapore, Singapore

fYear

2007

fDate

12-14 Dec. 2007

Firstpage

4693

Lastpage

4698

Abstract

In this paper, adaptive neural network control is investigated for a general class of strict-feedback systems using "ISS-modular" approach. The closed-loop system consists of two interconnected subsystems: the state error subsystem and the weight estimation subsystem. First, a neural controller is designed to achieve ISS for the state error subsystem with respect to the neural weight estimation errors. Then, a neural weight estimator is designed to achieve ISS for the weight estimation subsystem with respect to the system state errors. Finally, the stability of the entire closed-loop system is guaranteed by the small-gain theorem. The "ISS-modular" approach avoids the construction of an overall Lyapunov function for the closed- loop system, and overcomes the controller singularity problem completely. The simulation studies demonstrate the effectiveness of the proposed control method.

Keywords

Lyapunov methods; adaptive control; closed loop systems; feedback; interconnected systems; neurocontrollers; ISS-modular approach; Lyapunov function; adaptive NN control; adaptive neural network control; closed-loop system; controller singularity problem; input-to-state stability analysis; interconnected subsystems; small-gain theorem; state error subsystem; strict-feedback systems; weight estimation subsystem; Adaptive control; Adaptive systems; Control systems; Error correction; Estimation error; Lyapunov method; Neural networks; Programmable control; Stability; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2007 46th IEEE Conference on

Conference_Location

New Orleans, LA

ISSN

0191-2216

Print_ISBN

978-1-4244-1497-0

Electronic_ISBN

0191-2216

Type

conf

DOI

10.1109/CDC.2007.4434132

Filename

4434132