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Software-in-the-Loop Combined Reinforcement Learning Method for Dynamic Response Analysis of FOWTs

Lookup NU author(s): Professor Zhiqiang Hu



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Floating offshore wind turbines (FOWTs) still face many challenges on how to better predict the dynamic responses. Artificial intelligence (AI) brings a new solution to overcome these challenges with intelligent strategies. A new AI technology-based method, named SADA, is proposed in this paper for the prediction of dynamic responses of FOWTs. Firstly, the methodology of SADA is introduced with the selection of Key Disciplinary Parameters (KDPs). The AI module in SADA was built in a coupled aero-hydro-servo-elastic in-house program DARwind and the policy decision is providedby the machine learning algorithms deep deterministic policy gradient (DDPG). Secondly, a set of basin experimental results of a Hywind Spar-type FOWT were employed to train the AI module. SADA weights KDPs by DDPG algorithms’ actor network and changes their values according to the training feedback of 6DOF motions of Hywind platform through comparing the DARwind simulation results and that of experimental data.Many other dynamic responses that cannot bemeasured in basin experiment could be predicted in higher accuracy with this intelligent DARwind. Finally, the case study of SADA method was conducted and the results demonstrated that the mean values of the platform’s motions can be predicted by AI-based DARwind with higher accuracy, for example the maximum error of surge motion is reduced by 21%. This proposed SADA method takes advantage of numerical-experimental method and the machine learningmethod, which brings a new and promising solution for overcoming the handicap impeding direct use of traditional basin experimental technology in FOWTs design.

Publication metadata

Author(s): Chen P, Chen J, Hu Z

Publication type: Article

Publication status: Published

Journal: Frontiers in Marine Science

Year: 2021

Volume: 7

Online publication date: 28/01/2021

Acceptance date: 24/12/2020

Date deposited: 28/01/2021

ISSN (electronic): 2296-7745

Publisher: Frontiers Research Foundation


DOI: 10.3389/fmars.2020.628225


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