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Tracking Control And Stabilization Of A Fractional Financial Risk System Using Novel Active Finite-Time Fault-Tolerant Controls

Author

Listed:
  • BO WANG

    (School of Electrical and Information Engineering, Xihua University, Chengdu 610039, China2School of Electronic Information and Automation, Aba Teachers University, Sichuan 623002, China)

  • HADI JAHANSHAHI

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, Canada R3T 5V6, Canada)

  • STELIOS BEKIROS

    (Department of Banking and Finance, FEMA, University of Malta, MSD 2080, Msida, Malta5Department of Economics, European University Institute, Via delle Fontanelle, 18, I-50014 Florence, Italy)

  • YU-MING CHU

    (Department of Mathematics, Huzhou University, Huzhou 313000, P. R. China7Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science and Technology, Changsha 410114, P. R. China)

  • J. F. GÓMEZ-AGUILAR

    (CONACyT-Tecnológico Nacional de, México/CENIDET. Interior Internado Palmira S/N, Col. Palmira, C.P. 62490, Cuernavaca, Morelos, México)

  • FAWAZ E. ALSAADI

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)

  • MADINI O. ALASSAFI

    (Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia)

Abstract

This paper introduces a fractional-order financial risk system for the first time. Employing well-known tools and analyses such as bifurcations diagrams and spectral entropy, the dynamical behaviors of the system associated with fractional derivative are investigated. The impacts of the fractional derivative on the system’s behavior and its dynamical feature are shown. Then, tracking control and stabilization of the systems are studied. As it is obvious, the existence of faults and failures in the process of control of financial and economic systems is undeniable — this issue necessitates applying proper control techniques for the systems. So as to achieve appropriate results in the control of fractional financial risk system, two finite-time fault-tolerant controllers are proposed, namely, finite-time active fault-tolerant control and finite-time passive fault-tolerant control. Not only do these techniques force the system to reach desired values in finite time, but also, the proposed techniques are robust against uncertainties, faults, and failures in actuators. Through finite-time observers, the effects of all uncertainties are taken to account in the active controller. Finally, numerical simulations of tracking control and stabilization are presented. For numerical simulations, the fractional financial risk system is considered to be in the presence of unknown disturbance as well as faults and failures in actuators. It is assumed that the system is in the presence of various types of actuator faults. Numerical results affirm the ability of the offered control techniques for pushing the states of the fractional-order risk system to the desired value in a short period of time.

Suggested Citation

  • Bo Wang & Hadi Jahanshahi & Stelios Bekiros & Yu-Ming Chu & J. F. Gã“Mez-Aguilar & Fawaz E. Alsaadi & Madini O. Alassafi, 2021. "Tracking Control And Stabilization Of A Fractional Financial Risk System Using Novel Active Finite-Time Fault-Tolerant Controls," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 29(06), pages 1-20, September.
    • Handle: RePEc:wsi:fracta:v:29:y:2021:i:06:n:s0218348x21501553
    DOI: 10.1142/S0218348X21501553
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    Citations

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    Cited by:

    1. Liu, Chongyang & Zhou, Tuo & Gong, Zhaohua & Yi, Xiaopeng & Teo, Kok Lay & Wang, Song, 2023. "Robust optimal control of nonlinear fractional systems," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    2. H. Mesgarani & Y. Esmaeelzade Aghdam & A. Beiranvand & J. F. Gómez-Aguilar, 2024. "A Novel Approach to Fuzzy Based Efficiency Assessment of a Financial System," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1609-1626, April.
    3. Qijia Yao & Hadi Jahanshahi & Larissa M. Batrancea & Naif D. Alotaibi & Mircea-Iosif Rus, 2022. "Fixed-Time Output-Constrained Synchronization of Unknown Chaotic Financial Systems Using Neural Learning," Mathematics, MDPI, vol. 10(19), pages 1-14, October.
    4. Alsaadi, Fawaz E. & Bekiros, Stelios & Yao, Qijia & Liu, Jinping & Jahanshahi, Hadi, 2023. "Achieving resilient chaos suppression and synchronization of fractional-order supply chains with fault-tolerant control," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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