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

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 Biological...

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Bibliographic Details
Published in:Journal of marine science and engineering 2024-01, Vol.12 (1), p.112
Main Authors: Xu, Donghao, Li, Zelin, Xin, Ping, Zhou, Xueqian
Format: Article
Language:English
Subjects:
Accuracy
Adaptation
Algorithms
Biologically Inspired Neural Network
Control systems design
Control theory
Controllers
Design
Ecosystem disturbance
Explosions
Laws, regulations and rules
Methods
Neural networks
non-singular terminal sliding mode control
nonlinear extended state observer
Observers
Position errors
Saturation
Sliding mode control
Slumping
Stability analysis
State observers
Surface craft
Thrusters
Tracking control
Trajectory control
trajectory tracking
underactuated unmanned surface vessels
Unmanned aerial vehicles
Velocity
Velocity errors
Vessels
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Summary: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.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12010112