The expansion of wind generation and the advance in deep learning have provided
feasibility for multisite wind power prediction motivated by spatiotemporal dependencies …

This paper presents a framework by which a data-driven optimization technique known as
Bayesian Optimization can be used for real-time optimal control. In particular, Bayesian …

This article presents a novel spatiotemporal optimization approach for vertical path planning
(ie, waypoint optimization) to maximize the net output power of an ocean current turbine …

This paper evaluates two strategies, deep reinforcement learning (DRL) and model
predictive control (MPC), for maximizing harnessed power from a lifting surface controlled …

This paper presents a novel data-driven nested optimization framework that addresses the
problem of coupling between plant and controller optimization. This optimization strategy is …

Reducing the carbon emissions of ships and increasing the utilization of marine renewable
energy are the important ways to achieve the goal of carbon neutrality in ocean engineering …

This paper presents an integrated path planning and tracking control framework for a marine
current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous …

Numerous mobile robotic applications require agents to persistently explore and exploit
spatiotemporally varying, partially observable environments. Ultimately, the mathematical …

This paper evaluates a strategy for using multiple energy-harvesting autonomous
underwater vehicles (AUVs) to extract hydrokinetic energy out of a spatiotemporally-varying …

This paper describes and evaluates, through data-driven simulation, a strategy for using a
relocatable autonomous underwater vehicle (AUV) with on-board turbines to extract …