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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
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    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.

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