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Reinforcement learning-based structural control of floating wind turbines
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Zhang, Jincheng, Zhao, Xiaowei and Wei, Xing (2020) Reinforcement learning-based structural control of floating wind turbines. IEEE Transactions on Systems, Man, and Cybernetics: Systems . pp. 1-11. doi:10.1109/TSMC.2020.3032622
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WRAP-Reinforcement-learning-based-structural-control-floating-wind-turbines-Zhang-2020.pdf - Accepted Version - Requires a PDF viewer. Download (3695Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TSMC.2020.3032622
Abstract
The structural control of floating wind turbines using active tuned mass damper is investigated in this article. To our knowledge, this is for the first time that reinforcement learning-based control approach is employed to this type of application. Specifically, an adaptive dynamic programming (ADP) algorithm is used to derive the optimal control law based on the nonlinear structural dynamics, and the large-scale machine learning platform Tensorflow is employed for the design and implementation of the neural network (NN) structure. Three fully connected NNs, i.e., a plant network, a critic network, and an action network, are included in the proposed NN structure. Their training requires the gradient information flowing through the whole network, which is tackled by automatic differentiation, a popular technique for deriving the gradients of complex networks automatically. While to our knowledge, the network structures in the existing literature are rather simple and the training of the hidden layer is usually ignored. This allows their gradients to be derived analytically, which is infeasible with complex network structures. Thus, automatic differentiation greatly improves the employed ADP algorithm's ability in solving complex problems. The simulation results of structural control of floating wind turbines show that ADP controller performs very well in both normal and extreme conditions, with the standard deviation of the platform pitch displacement being reduced by around 40%. A clear advantage of ADP controllers over the H∞ controller is observed, especially in extreme conditions. Moreover, our design considers the tradeoff between the control performance and power consumption.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Dynamic programming, Reinforcement learning , Neural networks (Computer science), Wind turbines , Wind turbines -- Aerodynamics | ||||||
| Journal or Publication Title: | IEEE Transactions on Systems, Man, and Cybernetics: Systems | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2168-2216 | ||||||
| Official Date: | 10 November 2020 | ||||||
| Dates: |
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| Date of first compliant deposit: | 16 November 2020 | ||||||
| Page Range: | pp. 1-11 | ||||||
| DOI: | 10.1109/TSMC.2020.3032622 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Publisher Statement: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| RIOXX Funder/Project Grant: |
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