Spatiotemporal Optimization for Vertical Path Planning of an Ocean Current Turbine

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dc.contributor.authorHasankhan, Arezooen
dc.contributor.authorTang, Yufeien
dc.contributor.authorVanZwieten, Jamesen
dc.contributor.authorSultan, Cornelen
dc.date.accessioned2024-02-02T18:12:29Zen
dc.date.available2024-02-02T18:12:29Zen
dc.date.issued2022-07-29en
dc.description.abstractThis article presents a novel spatiotemporal optimization approach for vertical path planning (i.e., waypoint optimization) to maximize the net output power of an ocean current turbine (OCT) under uncertain ocean velocities. To determine the net power, OCT power generation from hydrokinetic energy and the power consumption for controlling the depth are modeled. The stochastic behavior of ocean velocities is a function of spatial and temporal parameters, which is modeled through a Gaussian process (GP) approach. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are applied to solve the formulated spatiotemporal optimization problem with constraints. Comparative studies show that the MPC- and RL-based methods are computationally feasible to address vertical path planning, which are evaluated with a baseline A∗ approach. Analysis of the robustness is further carried out under the inaccurate ocean velocity predictions. Results verify the efficiency of the presented methods in finding the optimal path to maximize the total power of an OCT system, where the total harnessed energy after 200 h shows over an 18% increase compared to the case without optimization.en
dc.description.versionAccepted versionen
dc.format.extentPages 587-601en
dc.format.extent15 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/TCST.2022.3193637en
dc.identifier.eissn1558-0865en
dc.identifier.issn1063-6536en
dc.identifier.issue2en
dc.identifier.orcidSultan, Cornel [0000-0002-1690-5125]en
dc.identifier.urihttps://hdl.handle.net/10919/117835en
dc.identifier.volume31en
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectModel predictive control (MPC)en
dc.subjectocean current turbine (OCT)en
dc.subjectreinforcement learning (RL)en
dc.subjectspatiotemporal optimizationen
dc.subjectvertical path planningen
dc.titleSpatiotemporal Optimization for Vertical Path Planning of an Ocean Current Turbineen
dc.title.serialIEEE Transactions on Control Systems Technologyen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dcterms.dateAccepted2022-02-14en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Aerospace and Ocean Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

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