Dynamic event-triggered adaptive positioning control of unmanned surface vehicles with system uncertainties

Abstract To solve the problems of unmanned surface vehicles (USVs), such as limited network bandwidth and computing resources, a finite-time command filtering backstepping (FTCFB) robust adaptive dynamic positioning control method with dynamic event-triggered mechanisms (DETM) for unmanned surface vehicles (USVs) is proposed. It addresses thruster dynamics, system uncertainties, and unknown external disturbances, ensuring finite-time convergence while mitigating issues like communication congestion and actuator wear. Adaptive neural networks are utilized to estimate the system uncertainties, and a DETM-based FTCFB control law, validated with Lyapunov stability theory, guarantees finite-time convergence of tracking and parameter estimation errors. Numerical simulations confirm the method’s effectiveness.