AI RESEARCH
Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms
arXiv CS.LG
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ArXi:2604.22797v1 Announce Type: cross Wind farm wake steering optimization is challenging due to complex flow physics and changing conditions. This paper presents a hierarchical framework that combines reinforcement learning with model predictive control, where an RL agent learns compensatory state estimates for an MPC controller, rather than directly controlling turbines. Evaluated on a three-turbine case, the approach achieves a 23\% power gain over the baseline control and surpasses the idealized MPC with perfect state knowledge.