Reinforcement Learning-Based Structural Control of Floating Wind Turbines
Jincheng Zhang, Xiaowei Zhao, Xing Wei
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 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> controller is observed, especially in extreme conditions. Moreover, our design considers the tradeoff between the control performance and power consumption.