Nonlinear decoration-driven adaptive neural finite-time control for USVs with two rotatable thrusters under false...

Area of Science:

• Robotics
• Control Systems Engineering
• Cybersecurity in Marine Systems

Background:

• Unmanned Surface Vessels (USVs) require advanced control for reliable navigation.
• Adverse network conditions, including false data injection attacks (FDIAs) and input saturation, pose significant challenges to USV control systems.
• Existing control strategies may not adequately address the combined effects of FDIAs and system constraints.

Purpose of the Study:

• To develop a robust trajectory tracking control scheme for USVs operating in challenging network environments.
• To mitigate the impact of false data injection attacks (FDIAs) and input saturation on USV performance.
• To ensure stable and accurate path following despite internal and external system uncertainties.

Main Methods:

• Implemented a hyperbolic tangent function for smooth control input transitions, preventing oscillation.
• Utilized stepwise reconstruction to address FDIAs in kinematic and dynamic channels.
• Employed a backstepping framework with virtual adaptive technology for kinematic channel disturbance compensation.
• Applied neural-based, high-speed disturbance compensation for dynamic channel uncertainties and FDIAs.
• Introduced adaptive finite-time control based on nonlinear decoration, analyzed using Lyapunov stability theory.

Main Results:

• The proposed control scheme ensures all signals within the closed-loop system remain bounded.
• Simulations demonstrate the effectiveness of the adaptive finite-time control strategy.
• USVs successfully tracked reference trajectories with satisfactory performance under FDIAs and input saturation.

Conclusions:

• The developed adaptive finite-time control scheme effectively enhances the robustness of USVs against cyber-attacks and system constraints.
• The control strategy provides a reliable solution for trajectory tracking in complex and uncertain marine environments.
• This research contributes to the advancement of secure and autonomous navigation for unmanned surface vessels.