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

Narayan Nayak, Soumya Ranjan Das, Tapas Kumar Panigrahi, Himansu Das, Soumya Ranjan Nayak, Krishna Kant Singh (), S. S. Askar and Mohamed Abouhawwash
Additional contact information
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

Mathematics, 2023, vol. 11, issue 8, 1-26

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.

Keywords: adaptive neuro-fuzzy inference system; autonomous underwater vehicle; fuzzy logic controller; neural network; self-tuning fuzzy-PID (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/8/1868/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/8/1868/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:8:p:1868-:d:1123694

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().