ASV Station Keeping under Wind Disturbances using Neural Network Simulation Error Minimization Model Predictive Control

📄 arXiv: 2310.07892v1 📥 PDF

作者: Jalil Chavez-Galaviz, Jianwen Li, Ajinkya Chaudhary, Nina Mahmoudian

分类: cs.RO, cs.LG

发布日期: 2023-10-11

💡 一句话要点

提出神经网络仿真误差最小化模型预测控制以解决ASV风干扰问题

🎯 匹配领域: 支柱一：机器人控制 (Robot Control)

关键词: 自主水面车辆 模型预测控制 神经网络 风干扰 动态预测 控制策略 实验验证

📋 核心要点

  1. 现有的反馈控制方法在应对ASV动态和环境干扰时缺乏准确性，导致站位保持困难。
  2. 本文提出的NNSEM-MPC利用神经网络来最小化仿真误差，从而提高对风干扰下ASV动态的预测能力。
  3. 实验结果表明，NNSEM-MPC在四个测试条件下表现优于其他控制器，且执行速度至少快36%。

📝 摘要（中文）

站位保持是自主水面车辆（ASV）在狭窄空间中进行调查时的关键操作，尤其是在需要保持相对位置的任务中。然而，传统反馈控制器在应对ASV动态和环境干扰时面临挑战。本文提出了一种基于神经网络仿真误差最小化的模型预测控制器（NNSEM-MPC），以准确预测ASV在风干扰下的动态。通过在ROS和Gazebo环境中进行的六组测试，结果显示NNSEM-MPC在大多数情况下优于其他控制方法，平均位置和航向误差分别降低了至少31%和46%。

🔬 方法详解

问题定义：本文旨在解决自主水面车辆（ASV）在风干扰下的站位保持问题。现有的反馈控制器依赖于准确的动态模型，面对环境干扰时表现不佳。

核心思路：论文提出的NNSEM-MPC通过神经网络来最小化仿真误差，从而提高对ASV动态的预测精度，增强其在风干扰下的控制能力。

技术框架：该方法的整体架构包括神经网络模型的训练、模型预测控制的实现以及实时反馈调整。主要模块包括环境建模、控制策略生成和执行反馈。

关键创新：NNSEM-MPC的核心创新在于将神经网络与模型预测控制相结合，显著提高了对动态变化的适应性，区别于传统控制方法的静态模型依赖。

关键设计：在设计中，网络结构采用了多层感知器，损失函数选择了均方误差，以优化预测精度。参数设置经过多次实验调整，以确保在不同风速和风向下的鲁棒性。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，NNSEM-MPC在六个测试条件中有四个表现优于其他控制器，平均位置误差降低了至少31%，航向误差降低了至少46%。此外，该方法的执行速度比其他模型预测控制器快至少36%。

🎯 应用场景

该研究的潜在应用领域包括海洋调查、环境监测和无人驾驶船舶的协同作业。通过提高ASV在风干扰下的控制精度，能够显著提升其在复杂环境中的作业效率和安全性，具有重要的实际价值和未来影响。

📄 摘要（原文）

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.