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<p>In order to observe rotational angles and speed of the three-dimensional autonomous underwater vehicles (AUVs) from the only information of AUVs’ positions with high performance, this paper proposes a new prescribed-time state (PTS) observer. The proposed observer has the capability of not only estimating unmeasured states of the AUVs in a prescribed time interval <i>T</i><sub><i>p</i></sub> but also performing under uncertain factors and unpredicted external disturbances. In the design procedure, the observer’s gain matrices are established elaborately to deal with the <i>T</i><sub><i>p</i></sub>-time estimation based on the algebraic Lyapunov-Sylvester matrix equation approach instead of only designing matrices to be Hurwitz in traditional solution, thereby facilitating observing AUVs even though the system is not observability. All the uncertain parameters and disturbances are combined into a unique vector and compensated by an online training radial basis function (RBF) neural network. The observation scheme constructed by the proposed prescribed-time observer and the RBF neural network is called online training network-based prescribed-time state observer. Mathematical solutions are proven theoretically sufficiently and their effectiveness is verified by numerical simulation in the digital platform.</p>

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An Online Training Network-based Prescribed-time State Observer for Uncertain Three-dimensional Autonomous Underwater Vehicles

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Tung Ngo Sy Thanh,
  • Dzung Manh Do,
  • Tuan Nguyen Van,
  • Hai Le Xuan,
  • Khoat Nguyen Duc

摘要

In order to observe rotational angles and speed of the three-dimensional autonomous underwater vehicles (AUVs) from the only information of AUVs’ positions with high performance, this paper proposes a new prescribed-time state (PTS) observer. The proposed observer has the capability of not only estimating unmeasured states of the AUVs in a prescribed time interval Tp but also performing under uncertain factors and unpredicted external disturbances. In the design procedure, the observer’s gain matrices are established elaborately to deal with the Tp-time estimation based on the algebraic Lyapunov-Sylvester matrix equation approach instead of only designing matrices to be Hurwitz in traditional solution, thereby facilitating observing AUVs even though the system is not observability. All the uncertain parameters and disturbances are combined into a unique vector and compensated by an online training radial basis function (RBF) neural network. The observation scheme constructed by the proposed prescribed-time observer and the RBF neural network is called online training network-based prescribed-time state observer. Mathematical solutions are proven theoretically sufficiently and their effectiveness is verified by numerical simulation in the digital platform.