用数据驱动方法优化无人潜航器速度控制，提升稳定性与安全性。
Optimizing AUV speed dynamics with a data-driven Koopman operator approach
- 基于数据驱动的柯尔莫哥洛夫算子理论建模非线性动力学
- 结合模型预测控制，在状态、输入及增量约束下实现稳定控制
- 适用于复杂水下环境，适合做潜航器控制研究者参考
自主水下航行器（AUV）在现代海洋探测中发挥着关键作用，其速度控制系统对其高效运行至关重要。与众多机器人系统类似，AUV具有多变量非线性动力学特性，并面临状态限制、输入限制以及增量输入约束，导致控制器设计困难且耗时。本文提出一种结合数据驱动柯尔莫哥洛夫算子理论与模型预测控制（MPC）的方法，综合考虑上述约束。该方法不仅在状态和输入受限条件下保证AUV性能，还通过考虑增量输入变化，防止操作中出现快速且可能造成损害的突变。此外，我们基于ROS2与Gazebo构建了验证平台，验证了所提算法的有效性，为应对复杂动态水下环境提供了新的控制策略。
原文摘要 · Abstract (English)
Autonomous Underwater Vehicles (AUVs) play an essential role in modern ocean exploration, and their speed control systems are fundamental to their efficient operation. Like many other robotic systems, AUVs exhibit multivariable nonlinear dynamics and face various constraints, including state limitations, input constraints, and constraints on the increment input, making controller design challenging and requiring significant effort and time. This paper addresses these challenges by employing a data-driven Koopman operator theory combined with Model Predictive Control (MPC), which takes into account the aforementioned constraints. The proposed approach not only ensures the performance of the AUV under state and input limitations but also considers the variation in incremental input to prevent rapid and potentially damaging changes to the vehicle's operation. Additionally, we develop a platform based on ROS2 and Gazebo to validate the effectiveness of the proposed algorithms, providing new control strategies for underwater vehicles against the complex and dynamic nature of underwater environments.
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