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Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems

by Imen Zaidi, Mohamed Chtourou, Mohamed Djemel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 10
Year of Publication: 2016
Authors: Imen Zaidi, Mohamed Chtourou, Mohamed Djemel
10.5120/ijca2016910377

Imen Zaidi, Mohamed Chtourou, Mohamed Djemel . Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems. International Journal of Computer Applications. 143, 10 ( Jun 2016), 23-30. DOI=10.5120/ijca2016910377

@article{ 10.5120/ijca2016910377,
author = { Imen Zaidi, Mohamed Chtourou, Mohamed Djemel },
title = { Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 10 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number10/25114-2016910377/ },
doi = { 10.5120/ijca2016910377 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:46:22.871032+05:30
%A Imen Zaidi
%A Mohamed Chtourou
%A Mohamed Djemel
%T Robust Neural Control Strategies for Discrete-Time Uncertain Nonlinear Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 10
%P 23-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, three neural control strategies are addressed to a class of single input-single output (SISO) discrete-time nonlinear systems affected by parametric variations. According to the control scheme, in a first step, a direct neural model (DNM) is developed to emulate the behavior of the system, then an inverse neural model (INM) is synthesized using specialized learning technique and cascaded to the system as a controller. The sliding mode backpropagation algorithm (SM-BP), which presents in a previous study robustness and high speed learning, is adopted for the training of the neural models. However, in the presence of strong parametric variations, the synthesized (INM) shows limitations to present satisfactory tracking performances. In fact, in order to improve the control results, two neural control strategies such as hybrid control and neuro-sliding mode control are proposed in this work. A simulation example is treated to show the effectiveness of the proposed control strategies

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Index Terms

Computer Science
Information Sciences

Keywords

SISO Discrete-time uncertain nonlinear systems neural modelling sliding mode backpropagation algorithm INM control hybrid control neuro-sliding mode control.

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