论文标题
数据驱动的机器学习模型，用于多目标拍打鳍的无人驾驶水下车辆控制系统
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System
论文作者
论文摘要
无人驾驶的水下车辆（UUV）推进系统为海军任务（例如监视和地形勘探）提供了高度机动性。最近的工作探索了时间序列神经网络替代模型的使用，以预测车辆设计和FIN运动学的推力。我们开发了一个基于搜索的逆模型，该模型利用运动学神经网络模型来控制系统设计。我们的反向模型找到了一组FIN运动学，其多目标目标是达到目标推力并在拍打周期之间创建光滑的运动学过渡。我们演示了整合此反向模型的控制系统如何使在线，周期周期调整以优先考虑不同的系统目标。
Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide high maneuverability for naval tasks such as surveillance and terrain exploration. Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics. We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. Our inverse model finds a set of fin kinematics with the multi-objective goal of reaching a target thrust and creating a smooth kinematic transition between flapping cycles. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.
