Distributed Learning-based MPC for Platooning Control of Heterogeneous Autonomous Surface Vehicles

Abstract

This paper presents a learning-based robust distributed model predictive control (DMPC) framework for heterogeneous autonomous surface vehicle (ASV) platoons subject to input bounds, matched disturbances, and inter-vehicle safety constraints. Each vessel solves a local, parallelizable MPC using exchanged assumed trajectories while enforcing a coupled minimum distance constraint. The control consists of a nominal tube-based model predictive control and an adaptive term produced online by a deep neural network that compensates the state-dependent uncertainties explicitly. A projection update and bounded activation layer guarantee a uniform bound on the network output, allowing it to be integrated into constraint tightening to ensure recursive feasibility and input-to-state (ISS) stability. Simulation results of a three-ASV platoon demonstrate safe spacing, accurate velocity tracking, and effective cancellation of matched disturbances, which confirms that embedding learning within a tube-based DMPC framework reduces conservativeness while retaining distributed scalability.