Adaptive Control Design of Neural Fuzzy System for NARMA-L2 Model

Author

Liu, Zhi ; Zhang, Yun

Author_Institution

Dept. of Autom., Guangdong Univ. of Technol., Guangzhou

Volume

1

fYear

0

fDate

0-0 0

Firstpage

2801

Lastpage

2805

Abstract

An adaptive neural control method is presented for the nonlinear discrete-time systems with the NARMA-L2 model. The neural fuzzy system is integrated with the approximate model-based control method to handle the nonlinear complexity, where the multiple fuzzy CMAC (MFCMAC) network is used to compensate the approximate NARMA model of the nonaffine nonlinear system. The weights of neural networks are modified by a novel adaptive algorithm, which guarantee the stability of the neural system without the persistent excitation requirement. The stability of the closed-loop system is proved with the Lyapunov method. Simulation results show that the method is effective

Keywords

Lyapunov methods; adaptive control; cerebellar model arithmetic computers; closed loop systems; compensation; control system synthesis; discrete time systems; fuzzy control; fuzzy neural nets; neurocontrollers; stability; Lyapunov method; NARMA-L2 Model; adaptive control design; approximate model-based control; closed-loop system; compensation; discrete-time systems; multiple fuzzy CMAC network; neural control; neural fuzzy system; nonaffine nonlinear system; nonlinear complexity; persistent excitation; stability; Adaptive algorithm; Adaptive control; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Stability; Nonlinear control; Persistent Excitation; adaptive control; neural networks;

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.2006.1712875

Filename

1712875