Adaptive Fuzzy Neural Network Control for a Class of Uncertain MIMO Nonlinear Systems via Sliding-mode design

被引：3

作者：

Wen Shenglin ^{[1
]}

Yan Ye ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Aerosp Sci & Engn, Changsha, Hunan, Peoples R China

来源：

2014 SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN-MACHINE SYSTEMS AND CYBERNETICS (IHMSC), VOL 2 | 2014年

关键词：

fuzzy neural network; sliding mode control; adaptive control; uncertain MIMO nonlinear system; IDENTIFICATION;

D O I：

10.1109/IHMSC.2014.124

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents an adaptive fuzzy neural network (FNN) control scheme for a class of uncertain multi-input multi-output (MIMO) nonlinear systems. A fuzzy neural network system is used to approximate the conventional sliding mode control (SMC) law. A supervisory compensator is introduced to eliminate the effect of the approximation error. The adaptive adjustment algorithms for fuzzy neural network parameters are derived in the sense of projection algorithm and Lyapunov stability theorem. Finally, the effectiveness of the proposed control scheme is demonstrated through the simulation of an uncertain MIMO nonlinear system.

引用

页码：87 / 92

页数：6

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