  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.
