Stable adaptive neuro-control design via Lyapunov function derivative estimation

被引：55

作者：

Rovithakis, GA ^{[1
]}

机构：

[1] Tech Univ Crete, Dept Elect & Comp Engn, Crete 73100, Greece

来源：

AUTOMATICA | 2001年 / 37卷 / 08期

关键词：

neural networks; neural control; robust adaptive control; resetting; nonlinear systems;

D O I：

10.1016/S0005-1098(01)00094-2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new approach to the tracking problem, for affine in the control nonlinear dynamical systems, whose nonlinearities are assumed to be unknown, is presented in this paper. The philosophy of the developed technique is based on estimating the derivative of an unknown Lyapunov function. exploiting the approximation capabilities of the linear in the weights neural network structures. A novel resetting strategy guarantees the boundedness away from zero of certain signals. The uniform ultimate boundedness of the tracking error to an arbitrarily small set, plus the boundedness of all other signals in the closed loop is guaranteed. (C) 2001 Elsevier Science Ltd. All rights reserved.

引用

页码：1213 / 1221

页数：9

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