Real-Time Vertical Path Planning Using Model Predictive Control for an Autonomous Marine Current Turbine
Real-Time Vertical Path Planning Using Model Predictive Control for an Autonomous Marine Current Turbine
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DOI：
10.1109/ccta49430.2022.9966028
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Arezoo Hasankhani;Yufei Tang;Yu Huang;James H. VanZwieten
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文献类型：
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作者：
Arezoo Hasankhani;Yufei Tang;Yu Huang;James H. VanZwieten
本文提出了一种预测的方法来解决实时的垂直路径规划的海流涡轮机（MCT）作为一个自主水下航行器（AUV），路径控制的目标是最大限度地提高总收获的海流能量。实时路径规划被制定为一系列的优化问题在一个预测地平线上的自主MCT模型和水下环境模型。利用现场采集的声学多普勒海流剖面仪（ADCP）数据训练时空神经网络（STNN），建立海流速度模型。提出了基于模型预测控制（MPC）的方法来解决优化，其中所提出的方法利用快速离散路径规划（即，网格化海洋环境中的路径规划）以寻求初始解，以及连续海洋环境中的连续路径规划以改进初始解。结果表明，所提出的增强连续路径规划算法可以找到更好的解决方案（即，最优路径）比独立连续路径规划。
This paper presents a predictive approach to address real-time vertical path planning for a marine current turbine (MCT) treated as an autonomous underwater vehicle (AUV), where the path control goal is to maximize the total harvested ocean current energy. The real-time path planning is formulated as a sequence of optimization problems over a prediction horizon with respect to the autonomous MCT model and underwater environment model. The ocean current velocity is modeled through a spatiotemporal neural network (STNN) trained using field-collected acoustic Doppler current profiler (ADCP) data. Model predictive control (MPC)-based approach is proposed to solve the optimizations, where the proposed approach takes advantage of fast discrete path planning (i.e., path planning in a gridded ocean environment) to seek the initial solution, as well as continuous path planning to improve the initial solution in a continuous ocean environment. Results demonstrate that the proposed reinforced continuous path planning algorithm can find a better solution (i.e., optimal path) than independent continuous path planning.