Controllability of Differential Systems with the General Conformable Derivative-Reference-Cited by-同舟云学术

Controllability of Differential Systems with the General Conformable Derivative

Published:2021-10-11 Issue: Volume:2021 Page:1-11
ISSN:1099-0526
Container-title:Complexity
language:en
Short-container-title:Complexity

Author:

Azouz Ferrag¹^ORCID,Boucenna Djalal¹²^ORCID,Ben Makhlouf Abdellatif³^ORCID,Mchiri Lassaad⁴^ORCID,Benchaabane Abbes⁵⁶^ORCID

Affiliation:

1. High School of Technological Teaching, Enset, Skikda, Algeria

2. Laboratory of Advanced Materials, Department of Mathematics Faculty of Sciences, Badji Mokhtar Annaba University, P.O. Box 12, Annaba 23000, Algeria

3. Department of Mathematics, College of Science, Jouf University, P.O. Box. 2014, Sakaka, Saudi Arabia

4. Department of Statistics and Operations Research, College of Sciences, King Saud University, Riyadh 11451, Saudi Arabia

5. Laboratory of Analysis and Control of Differential Equations “ACED”, Univ. 8 May 1945 Guelma, Guelma, Algeria

6. 8 May 1945 University, Guelma 24000, Algeria

Abstract

In this paper, the controllability of differential systems with the general conformable derivative is studied. By elaborating the rank criterion and the conformable Gram criterion, sufficient and necessary conditions to investigate that a linear general conformable system is null completely controllable are given. We obtain a full generalization to the general conformable fractional-order system case. In addition, Krasnoselskii’s fixed point theorem to obtain a complete controllability result for a semilinear general conformable system is applied.

Funder

Al Jouf University

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Link

http://downloads.hindawi.com/journals/complexity/2021/2817092.pdf

Reference23 articles.

1. A New Fractional Order Observer Design for Fractional Order Nonlinear Systems

2. Adapting H-infinity controller for the desired reference tracking of the sphere position in the maglev process

3. PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator