Station keeping is an essential maneuver for Autonomous Surface Vehicles (ASVs), mainly when used in confined spaces, to carry out surveys that require the ASV to keep its position or in collaboration with other vehicles where the relative position has an impact over the mission. However, this maneuver can become challenging for classic feedback controllers due to the need for an accurate model of the ASV dynamics and the environmental disturbances. This work proposes a Model Predictive Controller using Neural Network Simulation Error Minimization (NNSEM-MPC) to accurately predict the dynamics of the ASV under wind disturbances. The performance of the proposed scheme under wind disturbances is tested and compared against other controllers in simulation, using the Robotics Operating System (ROS) and the multipurpose simulation environment Gazebo. A set of six tests were conducted by combining two wind speeds (3 m/s and 6 m/s) and three wind directions (0$^\circ$, 90$^\circ$, and 180$^\circ$). The simulation results clearly show the advantage of the NNSEM-MPC over the following methods: backstepping controller, sliding mode controller, simplified dynamics MPC (SD-MPC), neural ordinary differential equation MPC (NODE-MPC), and knowledge-based NODE MPC (KNODE-MPC). The proposed NNSEM-MPC approach performs better than the rest in 4 out of the 6 test conditions, and it is the second best in the 2 remaining test cases, reducing the mean position and heading error by at least 31\% and 46\% respectively across all the test cases. In terms of execution speed, the proposed NNSEM-MPC is at least 36\% faster than the rest of the MPC controllers. The field experiments on two different ASV platforms showed that ASVs can effectively keep the station utilizing the proposed method, with a position error as low as $1.68$ m and a heading error as low as $6.14^{\circ}$ within time windows of at least $150$s.

翻译：定位保持是自主水面艇（ASVs）在受限空间内执行需要保持位置的测量任务、或与其他航行器协同作业（其中相对位置影响任务成败）时的一项关键操作。然而，由于需要精确的ASV动力学模型和扰动建模，该操作对传统反馈控制器构成挑战。本文提出了一种利用神经网络仿真误差最小化的模型预测控制器（NNSEM-MPC），以在风扰动下精确预测ASV动力学特性。通过机器人操作系统（ROS）和多用途仿真环境Gazebo，对所提方案在风扰动下的性能进行了仿真测试，并与其它控制器进行了对比。实验组合了两种风速（3 m/s和6 m/s）与三种风向（0$^\circ$、90$^\circ$和180$^\circ$）共六组测试。仿真结果明确显示NNSEM-MPC相较于以下方法具有优势：反步控制器、滑模控制器、简化动力学MPC（SD-MPC）、神经常微分方程MPC（NODE-MPC）以及知识增强NODE MPC（KNODE-MPC）。所提NNSEM-MPC方法在6种测试条件中的4种中表现最佳，在剩余2种测试中排名第二，所有测试案例的平均位置误差和航向误差分别降低了至少31%和46%。在计算速度方面，所提NNSEM-MPC相比其他MPC控制器至少快36%。在两艘不同ASV平台上的现场实验表明，利用所提方法ASV能有效实现定位保持，在至少150秒的时间窗口内位置误差低至1.68米，航向误差低至6.14$^{\circ}$。