Robustifying nonlinear systems using high-order neural network controllers

被引：15

作者：

Rovithakis, GA ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 1999年 / 44卷 / 01期

关键词：

neural networks; robust nonlinear adaptive control;

D O I：

10.1109/9.739082

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 robustifying control methodology for affine in the control nonlinear dynamical systems is developed in this paper. A correction control signal is added to a nominal controller (designed to guarantee a desired performance for the corresponding nominal system), to render the actual system uniformly ultimately hounded The control signal is smooth and does not require the a priori knowledge of an upper bound on the modeling error and/or optimal weight values. Simulations performed on a simple nonlinear system illustrate the approach.

引用

页码：102 / 108

页数：7

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