IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i8p1868-d1123694.html
   My bibliography  Save this article

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

Author

Listed:
  • Narayan Nayak

    (Department of Electronics and Instrumentation Engineering, Silicon Institute of Technology, Bhubaneswar 751024, India)

  • Soumya Ranjan Das

    (Department of Electrical Engineering, Parala Maharaja Engineering College, Berhampur 761003, India)

  • Tapas Kumar Panigrahi

    (Department of Electrical Engineering, Parala Maharaja Engineering College, Berhampur 761003, India)

  • Himansu Das

    (School of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, India)

  • Soumya Ranjan Nayak

    (School of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar 751024, India)

  • Krishna Kant Singh

    (Department of CSE, ASET, Amity University Uttar Pradesh, Noida 201313, India)

  • S. S. Askar

    (Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

  • Mohamed Abouhawwash

    (Department of Computational Mathematics, Science and Engineering (CMSE), College of Engineering, Michigan State University, East Lansing, MI 48824, USA
    Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt)

Abstract

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

Suggested Citation

  • Narayan Nayak & Soumya Ranjan Das & Tapas Kumar Panigrahi & Himansu Das & Soumya Ranjan Nayak & Krishna Kant Singh & S. S. Askar & Mohamed Abouhawwash, 2023. "Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle," Mathematics, MDPI, vol. 11(8), pages 1-26, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1868-:d:1123694
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/8/1868/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/8/1868/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tianlei Wang & Zhenxing Sun & Yun Ke & Chaochao Li & Jiwei Hu, 2023. "Two-Step Adaptive Control for Planar Type Docking of Autonomous Underwater Vehicle," Mathematics, MDPI, vol. 11(16), pages 1-17, August.

    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:11:y:2023:i:8:p:1868-:d:1123694. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.