Global fixed-time adaptive fuzzy path following control for unmanned surface vehicles subject to lumped uncertainties and actuator saturation

Fan, Yunsheng; Shi, Yaolin; Mu, Dongdong; Liu, J; Li, Zhihao

doi:10.1016/j.oceaneng.2023.115533

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…The derivative of Equation ( 33) is given by . (34) where τ du = τ du − τdu is the disturbance error, and β 1 p 1 q 1 (u e ) p 1 /q 1 −1 ≥ 0. It follows Equation ( 26) that the observation error of the NESO converges, and errors are bounded so that τ du is stable and converges to zero.…”

Section: Surge Motion Controller Design Using Biologically Inspired N...mentioning

confidence: 99%

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The Non-Singular Terminal Sliding Mode Control of Underactuated Unmanned Surface Vessels Using Biologically Inspired Neural Network

Xu,

Li,

Xin

et al. 2024

JMSE

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Underactuated Unmanned Surface Vessels (USVs) are widely used in civil and military fields due to their small size and high flexibility, and trajectory tracking control is a critical research area for underactuated USVs. This paper proposes a trajectory tracking control strategy using the Biologically Inspired Neural Network (BINN) for USVs to improve tracking speed and accuracy. A virtual control law is designed to obtain the required virtual velocity for trajectory tracking control, in which the velocity error is calibrated to ensure that the position error converges to zero. To observe and compensate for unknown and complex environmental disturbances such as wind, waves, and currents, a nonlinear extended state observer (NESO) is designed. Then, a controller based on Non-singular Terminal Sliding Mode (NTSM) is designed to resolve the problems of singular value and controller chattering and to improve the controller response speed. A BINN is introduced to simplify the process of differentiation, reduce the input values of the initial state, and solve the problem of thruster input saturation. Finally, the Lyapunov stability theory is utilized to analyze the stability of the proposed algorithm. The simulation results show that the proposed algorithm has a higher trajectory tracking accuracy and speed than traditional methods.

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Section: Surge Motion Controller Design Using Biologically Inspired N...mentioning

confidence: 99%

“…To address the vulnerability of underactuated USVs to unknown external environmental disturbances, researchers have proposed several methods in the literature. These methods include fuzzy control [34], neural network control, and disturbance observer [35]. An FTSMC based on a disturbance observer approach was proposed [36].…”

Section: Introductionmentioning

confidence: 99%