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<p>Achieving precise trajectory tracking is vital for underwater navigation, particularly under the influence of environmental disturbances and model uncertainties. This study presents a nonlinear model predictive control (NMPC) strategy for a multi-joint autonomous underwater vehicle (MJ-AUV) designed to maintain accurate trajectory tracking in the presence of time-varying disturbances. To enhance robustness, a nonlinear disturbance observer (NDO) is integrated to mitigate the effects of disturbances and uncertainties. The research begins by developing a discrete-time nonlinear model for the MJ-AUV, followed by the formulation of the NDO and NMPC. Key contributions of this work include the integration of NDO to counteract the effects of environmental disturbances and model uncertainties, which are common challenges in underwater operations. In addition, the Lyapunov-based stability analysis is performed, which guarantees the asymptotic convergence of position and velocity tracking errors. Numerical simulations under random uncertainties validate the effectiveness of the proposed NDO-NMPC controller, demonstrating improved maneuverability and tracking precision for the MJ-AUV. The results confirm that the combined NDO-NMPC approach offers robust and stable control, enabling the MJ-AUV to navigate complex trajectories with high accuracy.</p>

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Enhanced trajectory tracking performance for MJ-AUV using NDO-NMPC techniques

  • 期刊论文

  • 发表日期：

    2025年3月4日

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  • Zakarya Omar,
  • Chao Ren,
  • Qing-Hao Meng

摘要

Achieving precise trajectory tracking is vital for underwater navigation, particularly under the influence of environmental disturbances and model uncertainties. This study presents a nonlinear model predictive control (NMPC) strategy for a multi-joint autonomous underwater vehicle (MJ-AUV) designed to maintain accurate trajectory tracking in the presence of time-varying disturbances. To enhance robustness, a nonlinear disturbance observer (NDO) is integrated to mitigate the effects of disturbances and uncertainties. The research begins by developing a discrete-time nonlinear model for the MJ-AUV, followed by the formulation of the NDO and NMPC. Key contributions of this work include the integration of NDO to counteract the effects of environmental disturbances and model uncertainties, which are common challenges in underwater operations. In addition, the Lyapunov-based stability analysis is performed, which guarantees the asymptotic convergence of position and velocity tracking errors. Numerical simulations under random uncertainties validate the effectiveness of the proposed NDO-NMPC controller, demonstrating improved maneuverability and tracking precision for the MJ-AUV. The results confirm that the combined NDO-NMPC approach offers robust and stable control, enabling the MJ-AUV to navigate complex trajectories with high accuracy.