Time-Optimal Coordination of Multiple Entering and Departing Ferries in Terminals Based on Decentralized Learning Model Predictive Control

In large cities with harbors or rivers, ferry services have been an important part of public transport. In practice, many ferry terminals are subject to geographical and environmental conditions, limited space resources of the terminal pool, narrow navigable waters of ships, which leads to constrained operations and increases the difficulty of docking when multiple ferries enter and depart from the terminal. In view of the distributed, real-time and dynamic characteristics of multi-ferry maneuvering, this paper proposes an decentralized, time-optimal control strategy to coordinate the maneuvering of multiple entering and departing ferries in restricted terminals. It combines system identification techniques with model predictive control (MPC) and integrates with an iterative learning control (ILC) framework. It is data-driven and does not require prior knowledge regrading ferry parameters and hydrodynamic coefficients. Based on the states and maneuver data of previous successfully performed trajectories, linear time-varying prediction models that approximates unknown ferry dynamics are constructed, and the coupled constraints among multiple ferries are decomposed with support vector machine (SVM). Therefore, it also does not require information exchange during ferry operations and has the merits of low communication demand, as only local information is required in each ferry controller. Simulation experiments are carried out to evaluate the effectiveness of the proposed method