mlsanityData to 14 Oct 2026

Hierarchical RL-MPC Control for Dynamic Wake Steering in Wind Farms

arXiv 2604.22797 ↗First posted 13 Apr 2026eess.SY cs.LG cs.SYcited by 0

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Abstract (arXiv)

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. Compared to direct RL control, the hybrid architecture maintains superior safety characteristics during training while achieving comparable performance with more stable control actions.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 11 references that are in the map. First posted on arXiv 13 Apr 2026.

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2 of 11 references.

References · 2

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2025Closed-loop model-predictive wind farm flow control under time-varying inflow using FLORIDyncited by 1

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  1. 2026Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations
  2. 2025Reinforcement Learning Increases Wind Farm Power Production by Enabling Closed-Loop Collaborative Control
  3. 2025How to craft a deep reinforcement learning policy for wind farm flow control
  4. 2026Load constrained wind farm flow control through multi-objective multi-agent reinforcement learning
  5. 2025WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control
  6. 2025Hierarchical RL-MPC for Demand Response Scheduling
  7. 2026Offline Reinforcement Learning for Wind Farm Control: A Wind Tunnel Study under Dynamic Wind Directions
  8. 2026A Systematic Review and Taxonomy of Reinforcement Learning-Model Predictive Control Integration for Linear Systems