Improving Transient Response in Adaptive Control of Nonlinear Systems

被引：2

作者：

George, Koshy ^{[1
]}

Subramanian, Karpagavalli ^{[1
]}

Sheshadhri, Nagashree ^{[2
]}

机构：

[1] PES Inst Technol, Dept Telecommun Engn, PES Ctr Intelligent Syst, Bangalore, Karnataka, India

[2] PES Univ Campus, PES Ctr Intelligent Syst, Bangalore, Karnataka, India

来源：

IFAC PAPERSONLINE | 2016年 / 49卷 / 01期

关键词：

Adaptive Control; Nonlinear Systems; Neural Networks; reed forward Networks; Parameter Estimation; SEQUENTIAL LEARNING ALGORITHM; UNIVERSAL APPROXIMATION; DYNAMICAL-SYSTEMS; NETWORKS;

D O I：

10.1016/j.ifacol.2016.03.131

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An important requirement in control systems is an acceptable transient response. When the underlying dynamical systems are unknown, a factor that contributes is how fast an algorithm can identify them. It is well-known that the back propagation algorithm has rather poor convergence properties. Consequently, it sometimes takes several thousand iterations before the transient response is satisfactory. In this paper, we show that a sequential variant of the extreme learning machine considerably improves the transient response. (C)2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：658 / 663

页数：6

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