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This paper presents an integrated path planning and tracking control ofmarine hydrokinetic energy harvesting devices. To address the highly nonlinearand uncertain oceanic environment, the path planner is designed based on areinforcement learning (RL) approach by fully exploring the historical oceancurrent profiles. The planner will search for a path to optimize a chosen costcriterion, such as maximizing the total harvested energy for a given time.Model predictive control (MPC) is then utilized to design the tracking controlfor the optimal path command from the planner subject to problem constraints.The planner and the tracking control are accommodated in an integratedframework to optimize these two parts in a real-time manner. The proposedapproach is validated on a marine current turbine (MCT) that executes verticalwaypoint path searching to maximize the net power due to spatiotemporaluncertainties in the ocean environment, as well as the path following via anMPC tracking controller to navigate the MCT to the optimal path. Resultsdemonstrate that the path planning increases harnessed power compared to thebaseline (i.e., maintaining MCT at an equilibrium depth), and the trackingcontroller can successfully follow the reference path under different shearprofiles.

Integrated Path Planning and Tracking Control of Marine Current Turbine in Uncertain Ocean Environments