Control of a Twin Rotor using Twin Delayed Deep Deterministic Policy Gradient (TD3)
Researchers have developed a reinforcement learning framework to control and stabilize a Twin Rotor Aerodynamic System (TRAS). The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was employed due to its suitability for continuous state and action spaces, bypassing the need for a system model. Simulation results demonstrated the RL controller's effectiveness, which was further validated against conventional PID controllers under wind disturbances and in real-world laboratory experiments. AI
IMPACT Demonstrates a novel application of reinforcement learning for complex aerodynamic system control.