Model Predictive and Reinforcement Learning Methods for Active Flow Control of an Airfoil with Dual-point Excitation of Plasma Actuators
This study investigates the effectiveness of Model Predictive Control (MPC) and Reinforcement Learning (RL) for active flow control over a NACA 4412 airfoil near static stall at Reynolds number 4*10^5. By systematically evaluating these strategies, the research addresses a critical gap in optimizing excitation frequency and improving response time in flow control. The work contributes to understanding RL adaptability and performance versus MPC in aerodynamic flow separation control. Numerical simulations of the Reynolds Averaged Navier-Stokes equations with the Scale-Adaptive Simulation turbulence model are used. Dielectric Barrier Discharge plasma actuators in dual-point excitation mode control flow separation. The study evaluates adaptive MPC, temporal difference RL (TDRL), and deep Q-learning (DQL) for optimizing excitation frequency and expediting stabilization. An integrated signal processing DQL approach is also examined. Adaptive MPC achieved Cl = 1.60 at 110 Hz but struggled near physical limits. RL optimized excitation frequencies, reaching Cl = 1.62 in under 2.5 s at 100 or 200 Hz. The study presents a novel RL - MPC comparison for active flow control with DBD actuators, contrasting with prior work focusing on MPC or RL alone. Using an online learning framework, RL methods dynamically adapt to real-time conditions. Evaluating adaptive MPC and RL together in this setup yields new insights into comparative performance in dynamic environments.