Distributed Co-Simulation of Reinforcement Learning Optimized Fuzzy PID Control of a 10-MW Wind Turbine Yaw System

To address the limited adaptability and tuning efficiency of conventional yaw controllers under turbulent wind conditions, this paper investigates a reinforcement learning (RL)–optimized fuzzy PID control scheme for offshore wind turbine yaw systems. A distributed real-time co-simulation framework is established, in which a high-fidelity OpenFAST wind turbine model is coupled with a Simulink-based controller via networked data exchange to reflect realistic sampling and communication constraints. The proposed controller is examined under IEC 61400-1–compliant normal and extreme turbulence wind scenarios and is compared with conventional PID, fuzzy PID, particle swarm optimization (PSO)–based fuzzy PID, gray wolf optimizer (GWO)–based fuzzy PID, and model predictive control (MPC) schemes. Simulation results indicate that the proposed method reduces yaw rate root mean square (RMS) by up to 40% and total yaw energy consumption by up to 41%, while maintaining yaw alignment accuracy under both operating conditions.