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Abstract <p>This study presents a mathematical modeling and numerical performance evaluation of a robust adaptive control strategy for the stabilization and trajectory tracking of a remotely operated underwater vehicle (ROV). Through the precise design and control of ROVs for seabed and dam inspections, these systems can efficiently substitute for human intervention, thereby eliminating the necessity to dewater structures during maintenance operations. The developed adaptive tracking controller leverages radial basis function neural networks (RBF NNs) to estimate the unknown nonlinear dynamics of the system. To further enhance robustness, The controller integrates sophisticated robust control strategies to correct modeling inaccuracies in the neural network and manage bounded external disturbances. The stability and performance of the system are rigorously validated through Lyapunov-based stability analysis. The effectiveness and dependability of the suggested method are demonstrated by means of extensive numerical simulations.</p>

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Robust Adaptive Controller Design Based on Neural Networks for a Remotely Operated Underwater Vehicle (ROV)

  • 期刊论文

  • 发表日期：

    2025年8月1日

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  • Z. Bellahcene,
  • A. Laidani,
  • A. Belherazem,
  • M. Bouhamida

摘要

Abstract

This study presents a mathematical modeling and numerical performance evaluation of a robust adaptive control strategy for the stabilization and trajectory tracking of a remotely operated underwater vehicle (ROV). Through the precise design and control of ROVs for seabed and dam inspections, these systems can efficiently substitute for human intervention, thereby eliminating the necessity to dewater structures during maintenance operations. The developed adaptive tracking controller leverages radial basis function neural networks (RBF NNs) to estimate the unknown nonlinear dynamics of the system. To further enhance robustness, The controller integrates sophisticated robust control strategies to correct modeling inaccuracies in the neural network and manage bounded external disturbances. The stability and performance of the system are rigorously validated through Lyapunov-based stability analysis. The effectiveness and dependability of the suggested method are demonstrated by means of extensive numerical simulations.