This paper presents an integrated path planning and tracking control of marine hydrokinetic energy harvesting devices. To address the highly nonlinear and uncertain oceanic environment, the path planner is designed based on a reinforcement learning (RL) approach by fully exploring the historical ocean current profiles. The planner will search for a path to optimize a chosen cost criterion, such as maximizing the total harvested energy for a given time. Model predictive control (MPC) is then utilized to design the tracking control for the optimal path command from the planner subject to problem constraints. The planner and the tracking control are accommodated in an integrated framework to optimize these two parts in a real-time manner. The proposed approach is validated on a marine current turbine (MCT) that executes vertical waypoint path searching to maximize the net power due to spatiotemporal uncertainties in the ocean environment, as well as the path following via an MPC tracking controller to navigate the MCT to the optimal path. Results demonstrate that the path planning increases harnessed power compared to the baseline (i.e., maintaining MCT at an equilibrium depth), and the tracking controller can successfully follow the reference path under different shear profiles.
翻译:本文介绍了对海洋水动力能源采集装置的综合路径规划和跟踪控制。为了应对高度非线性和不确定的海洋环境,路径规划仪的设计基于一种强化学习(RL)方法,全面探索历史海洋洋流剖面。计划仪将寻找一条优化选定成本标准的途径,如在一定时间内最大限度地增加总收获能量。然后,模型预测控制(MPC)用于设计对受问题制约的来自规划员的最佳路径指令的跟踪控制。计划仪和跟踪控制器被安置在一个综合框架中,以便实时优化这两个部分。拟议方法在一条洋流涡轮机(MCT)上得到验证,该涡轮机将执行垂直路点路径,以尽量扩大由于海洋环境的瞬间不确定性而产生的净能量,并通过一个MPC跟踪控制器将MCT引向最佳路径。结果显示,路径规划过程比基线(即保持平衡深度的MCT)提高了利用能力,跟踪控制器可以成功地遵循不同轮图下的参考路径。