IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2023i1p15-d1304270.html
   My bibliography  Save this article

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

Author

Listed:
  • Houssem Jerbi

    (Department of Industrial Engineering, College of Engineering, University of Hail, Háil 81481, Saudi Arabia)

  • Obaid Alshammari

    (Department of Electrical Engineering, College of Engineering, University of Hail, Háil 81481, Saudi Arabia)

  • Sondess Ben Aoun

    (Department of Computer Engineering, College of Computer Science and Engineering, University of Hail, Háil 81451, Saudi Arabia)

  • Mourad Kchaou

    (Department of Electrical Engineering, College of Engineering, University of Hail, Háil 81481, Saudi Arabia)

  • Theodore E. Simos

    (Laboratory of Interdisciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
    Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, West Mishref 32093, Kuwait
    Data Recovery Key Laboratory of Sichun Province, Neijing Normal University, Neijiang 641100, China)

  • Spyridon D. Mourtas

    (Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, 10559 Athens, Greece
    Laboratory “Hybrid Methods of Modelling and Optimization in Complex Systems”, Siberian Federal University, 660041 Krasnoyarsk, Russia)

  • Vasilios N. Katsikis

    (Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, 10559 Athens, Greece)

Abstract

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.

Suggested Citation

  • Houssem Jerbi & Obaid Alshammari & Sondess Ben Aoun & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
  • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:15-:d:1304270
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/1/15/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/1/15/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Vladislav N. Kovalnogov & Ruslan V. Fedorov & Dmitry A. Generalov & Andrey V. Chukalin & Vasilios N. Katsikis & Spyridon D. Mourtas & Theodore E. Simos, 2022. "Portfolio Insurance through Error-Correction Neural Networks," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    2. Ben Hambly & Renyuan Xu & Huining Yang, 2020. "Policy Gradient Methods for the Noisy Linear Quadratic Regulator over a Finite Horizon," Papers 2011.10300, arXiv.org, revised Jun 2021.
    3. Spyridon D. Mourtas & Chrysostomos Kasimis, 2022. "Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-20, August.
    4. Rabeh Abbassi & Houssem Jerbi & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    5. Hadeel Alharbi & Houssem Jerbi & Mourad Kchaou & Rabeh Abbassi & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks," Mathematics, MDPI, vol. 11(3), pages 1-14, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xingyuan Li & Chia-Liang Lin & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2022. "Computation of Time-Varying {2,3}- and {2,4}-Inverses through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    2. Rabeh Abbassi & Houssem Jerbi & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking," Mathematics, MDPI, vol. 11(12), pages 1-21, June.
    3. Stanimirović, Predrag S. & Mourtas, Spyridon D. & Mosić, Dijana & Katsikis, Vasilios N. & Cao, Xinwei & Li, Shuai, 2024. "Zeroing neural network approaches for computing time-varying minimal rank outer inverse," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    4. Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
    5. Katsikis, Vasilios N. & Mourtas, Spyridon D. & Stanimirović, Predrag S. & Li, Shuai & Cao, Xinwei, 2023. "Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    6. Houssem Jerbi & Hadeel Alharbi & Mohamed Omri & Lotfi Ladhar & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2022. "Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations," Mathematics, MDPI, vol. 10(23), pages 1-16, November.
    7. Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Jose A. Olivas, 2023. "A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    8. Anthony Coache & Sebastian Jaimungal, 2021. "Reinforcement Learning with Dynamic Convex Risk Measures," Papers 2112.13414, arXiv.org, revised Nov 2022.
    9. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
    10. Jianrong Chen & Xiangui Kang & Yunong Zhang, 2023. "Continuous and Discrete ZND Models with Aid of Eleven Instants for Complex QR Decomposition of Time-Varying Matrices," Mathematics, MDPI, vol. 11(15), pages 1-18, July.
    11. Jodi Dianetti & Giorgio Ferrari & Renyuan Xu, 2024. "Exploratory Optimal Stopping: A Singular Control Formulation," Papers 2408.09335, arXiv.org, revised Oct 2024.
    12. Jialun Cao & David v{S}iv{s}ka & Lukasz Szpruch & Tanut Treetanthiploet, 2024. "Logarithmic regret in the ergodic Avellaneda-Stoikov market making model," Papers 2409.02025, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:15-:d:1304270. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.