Robust distributed model predictive platooning control for heterogeneous autonomous surface vehicles

This paper proposes to apply a robust distributed model predictive platooning control approach for a group of heterogeneous autonomous surface vehicles (ASVs) with the input constraint and bounded external disturbances. The control input for each ASV is composed of two parts: the optimal nominal control input and the ancillary control input. The optimal nominal control input is generated by solving a distributed MPC (DMPC) problem based on the state information of itself and its neighbors. The offline ancillary control law aims to ensure that the actual system state trajectory evolves in a hyper-tube centered along the optimal nominal state trajectory. A coupled inter-vehicle safety constraint is designed for the DMPC optimization problem to guarantee the inter-ASV collision avoidance. Theoretical results on ensuring the feasibility of the proposed robust DMPC algorithm are provided and the closed-loop systems are proved to be input-to-state stable. Numerical simulations are performed to verify the theoretical results.