Cooperative Formation Control of MSVs via Hierarchical Learning Architecture

This paper addresses the cooperative formation control problem in the context of path-following for a fleet of multiple surface vehicles (MSVs). To address environmental uncertainties and system complexities, this paper proposes a hierarchical control framework that integrates model predictive control (MPC) and adaptive reinforcement learning (ARL) controllers. The MPC subsystem is designed to optimize the nonlinear dynamics of each surface vehicle, ensuring trajectory tracking by solving an appropriate optimization problem. Meanwhile, the ARL controller employs an indirect learning approach to handle the time-varying characteristics of the system, adapting to dynamic uncertainties and external disturbances. The effectiveness and robustness of the proposed control protocol are validated through comprehensive simulations, demonstrating its ability to achieve precise coordination and reliable path-following performance under complex conditions.