Decentralized Adaptive Control of Large-Scale Nonaffine Nonlinear Systems Using Radial Basis Function Neural Networks

Bahram KARIMI; Mohammad Bagher MENHAJ; Iman SABOORI

doi:10.1093/ietfec/e90-a.10.2239

Decentralized Adaptive Control of Large-Scale Nonaffine Nonlinear Systems Using Radial Basis Function Neural Networks

Bahram KARIMI, Mohammad Bagher MENHAJ, Iman SABOORI

Full Text Views

0

Share
Cite this

Summary :

In this paper, a novel decentralized adaptive neural network controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, nonaffine subsystems and unknown nonlinear interconnections. The stability of the closed loop system is guaranteed by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed method, we performed some simulation studies. The results of simulation become very promising.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.10 pp.2239-2247

Publication Date: 2007/10/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e90-a.10.2239

Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)

Category: Systems Theory and Control

Cite this

Copy

Bahram KARIMI, Mohammad Bagher MENHAJ, Iman SABOORI, "Decentralized Adaptive Control of Large-Scale Nonaffine Nonlinear Systems Using Radial Basis Function Neural Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E90-A, no. 10, pp. 2239-2247, October 2007, doi: 10.1093/ietfec/e90-a.10.2239.
Abstract: In this paper, a novel decentralized adaptive neural network controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, nonaffine subsystems and unknown nonlinear interconnections. The stability of the closed loop system is guaranteed by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed method, we performed some simulation studies. The results of simulation become very promising.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.10.2239/_p

Copy

@ARTICLE{e90-a_10_2239,
author={Bahram KARIMI, Mohammad Bagher MENHAJ, Iman SABOORI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Decentralized Adaptive Control of Large-Scale Nonaffine Nonlinear Systems Using Radial Basis Function Neural Networks},
year={2007},
volume={E90-A},
number={10},
pages={2239-2247},
abstract={In this paper, a novel decentralized adaptive neural network controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, nonaffine subsystems and unknown nonlinear interconnections. The stability of the closed loop system is guaranteed by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed method, we performed some simulation studies. The results of simulation become very promising.},
keywords={},
doi={10.1093/ietfec/e90-a.10.2239},
ISSN={1745-1337},
month={October},}

Copy

TY - JOUR
TI - Decentralized Adaptive Control of Large-Scale Nonaffine Nonlinear Systems Using Radial Basis Function Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2239
EP - 2247
AU - Bahram KARIMI
AU - Mohammad Bagher MENHAJ
AU - Iman SABOORI
PY - 2007
DO - 10.1093/ietfec/e90-a.10.2239
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2007
AB - In this paper, a novel decentralized adaptive neural network controller is proposed for a class of large-scale nonlinear systems with unknown nonlinear, nonaffine subsystems and unknown nonlinear interconnections. The stability of the closed loop system is guaranteed by introducing a robust adaptive bound based on Lyapunov stability analysis. A radial-basis function type neural network is used in the paper. To show the effectiveness of the proposed method, we performed some simulation studies. The results of simulation become very promising.
ER -