RHONN identifier-control scheme for nonlinear discrete-time systems with unknown time-delays

被引：14
|
作者：
Rios, Jorge D. [1 ]
Alanis, Alma Y. [1 ]
Lopez-Franco, Carlos [1 ]
Arana-Daniel, Nancy [1 ]
机构：
[1] Univ Guadalajara, Ctr Univ Ciencias Exactas & Ingn, Blvd Marcelino Garca Barragn 1421, Guadalajara 44430, Jalisco, Mexico
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2018年 / 355卷 / 01期
关键词：
MARKOVIAN JUMP SYSTEMS; VARYING DELAY; TRACKING;
D O I：
10.1016/j.jfranklin.2017.11.014
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This work presents a neural identifier-control scheme for uncertain nonlinear discrete-time systems with unknown time-delays. This scheme is based on a neural identifier to get a model of the system and a discrete-time block control technique based on sliding modes to generate the control law. The neural identifier is based on a Recurrent High Order Neural Network (RHONN) trained with an Extended Kalman Filter (EKF) based algorithm. Applicability is shown using real-time test results for linear induction motors. Also, a Lyapunov analysis is added in order to prove the semi-globally uniformly ultimately boundedness (SGUUB) of the proposed neural identifier-control scheme. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：218 / 249
页数：32
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