Event-Triggered Approximate Optimal Path-Following Control for Unmanned Surface Vehicles With State Constraints

Area of Science:

• Robotics
• Control Systems
• Marine Engineering

Background:

• Unmanned Surface Vehicles (USVs) face challenges in path following due to underactuation and state constraints.
• Existing control methods like Barrier Lyapunov Functions (BLF) can be complex and computationally intensive.

Purpose of the Study:

• To develop a novel control algorithm for robust path following in underactuated USVs with state constraints.
• To enhance control performance and reduce computational and communication burdens.

Main Methods:

• A Guidance-based Path-Following (GBPF) principle is integrated to address singularity issues.
• A nonlinear mapping transforms the constrained USV model into an unconstrained system.
• Adaptive Dynamic Programming (ADP) with a critic Neural Network (NN) approximates optimal control.
• An event-triggered mechanism is employed to optimize control updates.

Main Results:

• The proposed method effectively handles state constraints without traditional BLF.
• The control scheme guarantees approximate optimal performance.
• The event-triggered approach significantly reduces communication and computational demands compared to time-triggered methods.
• Stability of the closed-loop system is mathematically proven.

Conclusions:

• The combined backstepping, ADP, and event-triggered control strategy offers an effective solution for USV path following.
• The approach demonstrates improved efficiency and robustness in handling state constraints.
• Simulation and experimental results validate the proposed method's effectiveness.