Enhancing AMV Path Following via Precise Dynamics Identification and Finite-Time Guidance and Control

In this paper, an integrated control framework is developed for unmanned marine vehicles, aiming to enhance path-following performance through precise dynamics identification and finite-time guidance and control. To overcome the noise sensitivity inherent in traditional least-squares methods, the Huber-loss optimization algorithm is employed, with its convexity ensuring convergence to a unique global minimum. For guidance, an improved finite-time state observer is introduced to accurately estimate surge and sway velocities, enabling high-precision line-of-sight guidance. In the control phase, a fast terminal sliding mode control strategy is designed to guarantee rapid convergence and finite-time stability. Moreover, chattering is effectively suppressed by adaptively adjusting the switching gains based on real-time estimates of model uncertainties. The effectiveness of the proposed framework is validated through both simulation and lake trials. Simulation results demonstrate exceptional performance, maintaining heading angle errors within 2<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula>, while lake experiments confirm high-accuracy path following, achieving a root mean square error of less than 0.9m.