Constrained Control of Autonomous Surface Vehicles for Multitarget Encirclement via Fuzzy Modeling and Neurodynamic Optimization

Abstract

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.