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

Abstract

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.

Event-Triggered Supercavitating Vehicle Terminal Sliding Mode Control Based on Non-Recursive Observation

“…et al [16,17] designed a robust station-keeping (SK) control algorithm based on sliding mode control (SMC) theory for underwater vehicles, which ensured the control stability and better performance of a hovering over-actuated autonomous underwater vehicle (HAUV). To observe and compensate for unknown and complex environmental disturbances such as wind, waves, and currents, D. X. et al [18] designed a nonlinear extended state observer (NESO). They then proposed a method that used a biologically inspired neural network (BINN) to reduce the input values of the initial state and solve the problem of thruster input saturation.…”