Wake losses in wind farms, caused by closely spaced turbines, reduce downstream power production and overall efficiency. These losses can be mitigated through wind farm flow control strategies, specifically wake steering, which involves yaw control to misalign turbines with incoming wind, and redirecting wakes to enhance overall power output. This thesis aims to develop a real-time, model-based controller that addresses operational and environmental challenges in large-scale wind farms. The controller adapts to off-design conditions such as changing atmospheric factors and offline turbines, making it more effective than traditional controllers based on pre-optimized lookup tables. The research modifies engineering wake models to account for heterogeneous wind conditions, improves the computational efficiency of the Serial-Refine optimization strategy for real-time use, and incorporates tuning of wake model parameters to ensure adaptability. Additionally, the impact of offline turbines on optimal yaw misalignment angles is examined. Results indicate that adapting wake models using sensor data improves power predictions in non-uniform wind environments, while the distributed optimization framework for real-time yaw adjustments performs comparably to centralized methods. Incorporating offline turbines into the control strategy further boosts power output, leading to increased revenue for the wind farm. This research advances wind farm flow control by developing control policies that are tailored to dynamic, real-world site conditions.