LAUSR

This article addresses the cooperative multitarget encircling control of underactuated autonomous surface vehicles with unknown kinetics subject to velocity and input constraints. A distributed observer is designed for the vehicles to estimate the geometric center of the area covered by multiple moving targets. Based on the target center estimate, a multitarget encircling guidance law is developed to form encircling trajectories around the targets. A data-driven fuzzy predictor is designed for learning the vehicle kinetics, including model input gains, with available data. Based on the learned model, a nominal control law is developed to track reference guidance signals. In order to satisfy the velocity and input constraints, a feasibility condition for velocities is derived based on a control barrier function, and a neurodynamics-based optimal control law is developed based on the feasibility condition and input constraint. The bounded input-to-state stability of the closed-loop control system is theoretically proved. Simulation results are elaborated to substantiate the effectiveness of the proposed control approach for circumnavigating multiple moving targets.