Data-driven docking control of autonomous double-ended ferries based on iterative learning model predictive control

Abstract

In large cities with harbors or rivers, ferry services have been a crucial part of public transport. Docking refers to the process of maneuvering a ferry to the designated mooring position by the quayside. To facilitate the transition towards increased autonomy in ferries, this paper proposes an iterative learning-based model predictive control (IL-MPC) strategy for the automatic docking of over-actuated double-ended ferries, in situations in which the hydrodynamic coefficients of a ferry are unknown. It uses the collected data from actual ferry docking operations or reliable simulations to build ferry states prediction model in a linear state-space form based on kernel regression, which reduces the model-solving complexity in MPC. Moreover, it improves its closed-loop control performance and modeling accuracy via an iterative learning scheme. The excitation control input signals have been optimized to create suitable initial data sets. Simulations are carried out considering three docking position types and results show that the open-loop prediction accuracy has been improved with the optimized excitation input signal and that closed-loop control performance has been enhanced with the increase of iterations.