Time-optimal coordination of multiple entering and departing ferries in terminals based on decentralized learning model predictive control

Abstract

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, and restricted navigable waters of ships, which leads to constrained operations and increases the difficulty of docking when multiple ferries enter and depart from the terminal. Because of the distributed, real-time and dynamic characteristics of multi-ferry maneuvering, this paper proposes a decentralized, time-optimal control strategy to coordinate the maneuvering of multiple entering and departing ferries in restricted terminals. It combines nonlinear model predictive control (MPC) with an iterative learning control (ILC) framework. Based on the states and maneuver data of previously successfully performed trajectories, the coupled constraints among multiple ferries are decomposed with support vector machine (SVM). Therefore, it also does not require real-time information exchange during ferry operations and has the merits of low communication demand, as only local information is required in each ferry controller. Simulations are carried out to evaluate the effectiveness of the proposed method.