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This paper focuses on the trajectory tracking control of an underactuated AUV in the vertical plane. First, an error analysis is conducted for the underactuated AUV in the vertical plane, and a Disturbance Observer (DO) is designed to estimate external disturbances, while a Radial Basis Function Neural Network (RBFNN) is employed to approximate nonlinear terms. Subsequently, controllers are designed based on Nonsingular Terminal Sliding Mode Control (NTSMC) and Nonsingular Fast Terminal Sliding Mode Control (NFTSMC), respectively. Finally, the stability of the controllers is verified using the Lyapunov function. Simulation results demonstrate that controllers effectively improve trajectory tracking accuracy and enhances the robustness of the system.

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Trajectory Tracking Control of Underactuated AUV Based on RBF Neural Network and Nonsingular Terminal Sliding Mode

  • 会议论文

  • 发表日期：

    2026年

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  • Yang Chen,
  • Huiyi Luo,
  • Weilin Luo

摘要

This paper focuses on the trajectory tracking control of an underactuated AUV in the vertical plane. First, an error analysis is conducted for the underactuated AUV in the vertical plane, and a Disturbance Observer (DO) is designed to estimate external disturbances, while a Radial Basis Function Neural Network (RBFNN) is employed to approximate nonlinear terms. Subsequently, controllers are designed based on Nonsingular Terminal Sliding Mode Control (NTSMC) and Nonsingular Fast Terminal Sliding Mode Control (NFTSMC), respectively. Finally, the stability of the controllers is verified using the Lyapunov function. Simulation results demonstrate that controllers effectively improve trajectory tracking accuracy and enhances the robustness of the system.