Adaptive Inverse Deep Reinforcement Lyapunov learning control for a floating wind turbine

Offshore floating wind turbines (FWT) decrease adverse climate change effects without occupying significant land and harvesting fields. Owing to the earth planet unexpected climate, online adaptive feedback control of FWTs will be effective in sense optimal uniform energy capture. In this paper, a deep reinforcement learning (DRL)-based system is proposed offset both disturbance noise effects. Large variations water waves generate enormous information give rise convergent neural networks model turbine. As result abrupt changes, an inverse equipped with DRL could easily cope inherent drawback i.e., tracking error. Furthermore, received rewards algorithm are passed through newly designed training predict actions such that loss function decreased. The attenuation on performance closed-loop FWT clarified software implementation tests while weight’s convergency update rules proved by direct Lyapunov theorem.