In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular design for ease of transfer of autonomy across different maritime surface vessel platforms. The data-driven approach alleviates issues related to a priori identification of system models that may become deficient under evolving system behaviors or shifting, unanticipated, environmental influences. Our proposed learning-based platform comprises a deep Koopman system model and a change point detector that provides guidance on domain shifts prompting relearning under severe exogenous and endogenous perturbations. Motion control of the autonomous system is achieved via an optimal controller design. The Koopman linearized model naturally lends itself to a linear-quadratic regulator (LQR) control design. We propose the C3D control architecture Cascade Control with Change Point Detection and Deep Koopman Learning. The framework is verified in station keeping task on an ASV in both simulation and real experiments. The approach achieved at least 13.9 percent improvement in mean distance error in all test cases compared to the methods that do not consider system changes.

翻译：本文探讨了一种在未知动态条件下能够在水域领域执行多种任务的鲁棒自主系统的开发与部署。我们研究了一种基于模块化设计的数据驱动方法，以便在不同水面航行器平台之间实现自主性的便捷迁移。该方法缓解了因系统行为演变或不可预见的环境影响而导致预先辨识的系统模型失效等问题。我们提出的学习平台由深度Koopman系统模型和变点检测器组成，后者可识别域迁移信号，从而在严重外源性和内源性扰动下触发模型重学习。自主系统的运动控制通过最优控制器实现。Koopman线性化模型天然适用于线性二次型调节器（LQR）控制设计。本文提出了C3D控制架构，即融合变点检测与深度Koopman学习的级联控制。该框架在自主水面航行器（ASV）的定点保持任务中进行了仿真与实船实验验证。与不考虑系统变化的方法相比，本方法在所有测试案例中的平均距离误差至少降低了13.9%。