Robust Design of Adaptive Neural Controllers for Unknown Nonlinear Systems

被引：0

作者：

Liu, Ziqian ^{[1
]}

Torres, Raul E. ^{[2
]}

Kotinis, Miltiadis ^{[3
]}

机构：

[1] SUNY Maritime Coll, Dept Engn, Throggs Neck, NY 10465 USA

[2] Univ Puerto Rico, Dept Elect & Comp Engn, Mayaguez 00681, PR USA

[3] Old Dominion Univ, Dept Mech Engn, Norfolk, VA 23529 USA

来源：

53RD IEEE INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS | 2010年

关键词：

IDENTIFICATION; NETWORK;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we extend our previous research results from the stabilization of dynamic neural networks to the stabilization of unknown nonlinear systems, and present an approach of H-infinity control for nonlinear systems via dynamic neural networks. The proposed H-infinity controller is intended to attenuate the adverse impact of modeling error, considered as a disturbance, to a prescribed level with stability margins. A numerical example demonstrates the performance of stabilizing control on an unstable unknown nonlinear system.

引用

页码：993 / 996

页数：4

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