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
收藏
页码：658 / 663
页数：6
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