Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control

The stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) me...

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Main Authors: Houssem Jerbi, Obaid Alshammari, Sondess Ben Aoun, Mourad Kchaou, Theodore E. Simos, Spyridon D. Mourtas, Vasilios N. Katsikis
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/12/1/15
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author Houssem Jerbi
Obaid Alshammari
Sondess Ben Aoun
Mourad Kchaou
Theodore E. Simos
Spyridon D. Mourtas
Vasilios N. Katsikis
author_facet Houssem Jerbi
Obaid Alshammari
Sondess Ben Aoun
Mourad Kchaou
Theodore E. Simos
Spyridon D. Mourtas
Vasilios N. Katsikis
author_sort Houssem Jerbi
collection DOAJ
description The stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) method, which has shown significant success at solving time-varying problems, is used to do this. We present a novel ZNN model called ’ZQ-ARE’ that effectively solves the TQARE by finding only Hermitian solutions. The model works quite effectively, as demonstrated by one application to quadrotor control and three simulation tests. Specifically, in three simulation tests, the ZQ-ARE model finds the TQARE Hermitian solution under various initial conditions, and we also demonstrate that the convergence rate of the solution can be adjusted. Furthermore, we show that adapting the ZQ-ARE solution to the state-dependent Riccati equation (SDRE) technique stabilizes a quadrotor’s flight control system faster than the traditional differential-algebraic Riccati equation solution.
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spelling doaj.art-26dd91a8f0584465af52158160236e2f2024-01-10T15:03:18ZengMDPI AGMathematics2227-73902023-12-011211510.3390/math12010015Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor ControlHoussem Jerbi0Obaid Alshammari1Sondess Ben Aoun2Mourad Kchaou3Theodore E. Simos4Spyridon D. Mourtas5Vasilios N. Katsikis6Department of Industrial Engineering, College of Engineering, University of Hail, Háil 81481, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, University of Hail, Háil 81481, Saudi ArabiaDepartment of Computer Engineering, College of Computer Science and Engineering, University of Hail, Háil 81451, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, University of Hail, Háil 81481, Saudi ArabiaLaboratory of Interdisciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, RussiaDepartment of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, 10559 Athens, GreeceDepartment of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, 10559 Athens, GreeceThe stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) method, which has shown significant success at solving time-varying problems, is used to do this. We present a novel ZNN model called ’ZQ-ARE’ that effectively solves the TQARE by finding only Hermitian solutions. The model works quite effectively, as demonstrated by one application to quadrotor control and three simulation tests. Specifically, in three simulation tests, the ZQ-ARE model finds the TQARE Hermitian solution under various initial conditions, and we also demonstrate that the convergence rate of the solution can be adjusted. Furthermore, we show that adapting the ZQ-ARE solution to the state-dependent Riccati equation (SDRE) technique stabilizes a quadrotor’s flight control system faster than the traditional differential-algebraic Riccati equation solution.https://www.mdpi.com/2227-7390/12/1/15zeroing neural networkquaternionalgebraic Riccati equationquadrotor control
spellingShingle Houssem Jerbi
Obaid Alshammari
Sondess Ben Aoun
Mourad Kchaou
Theodore E. Simos
Spyridon D. Mourtas
Vasilios N. Katsikis
Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control
Mathematics
zeroing neural network
quaternion
algebraic Riccati equation
quadrotor control
title Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control
title_full Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control
title_fullStr Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control
title_full_unstemmed Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control
title_short Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control
title_sort hermitian solutions of the quaternion algebraic riccati equations through zeroing neural networks with application to quadrotor control
topic zeroing neural network
quaternion
algebraic Riccati equation
quadrotor control
url https://www.mdpi.com/2227-7390/12/1/15
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