Inland waterway transportation is one of the most important means to transport cargo in rivers and canals. To facilitate autonomous navigation for ships in inland waterways, this paper proposes a data-driven approach for predictions and control of underactuated ships with unknown dynamics, which integrates model predictive control (MPC) with an iterative learning control (ILC) scheme. In each iteration, kernel-based linear regressors are used to identify the relations between the evolution of ship states and control inputs based on the stored data from previous iterations and the collected data during operation, so as to build the system prediction model. The data are dynamically used to fix the prediction model over iterations, as well as to improve the controller performance until it converges. The proposed approach does not require prior knowledge regarding the hydrodynamic coefficients and ship parameters, but learns from the data instead. In addition, it exploits the advantages of MPC in handling constraints with minimised overall cost. Simulation results show that the controller could start from a nominal, linear data-driven ship model and then learn to reduce the path-following errors based on the data obtained over iterations.

内河运输是江河运河中最重要的货物运输方式之一。为了促进内河船舶的自主航行，本文提出了一种数据驱动的方法来预测和控制未知动力学的欠驱动船舶，该方法将模型预测控制 (MPC) 与迭代学习控制 (ILC) 方案相结合。在每次迭代中，基于核的线性回归器根据先前迭代存储的数据和运行期间收集的数据来识别船舶状态演化与控制输入之间的关系，从而建立系统预测模型。数据动态地用于在迭代过程中修复预测模型，以及提高控制器性能直到它收敛。所提出的方法不需要有关水动力系数和船舶参数的先验知识，而是从数据中学习。此外，它利用 MPC 在处理约束方面的优势，同时最大限度地降低了总成本。仿真结果表明，控制器可以从名义上的线性数据驱动船舶模型开始，然后根据迭代获得的数据学习减少路径跟踪误差。