A Study of Aero-Engine Control Method Based on Deep Reinforcement Learning

A novel aero-engine control method based on deep reinforcement learning (DRL) is proposed to improve the engine response ability. The Q-learning that is model free and can be performed online is adopted. For improving the learning capacity of DRL, the online sliding window deep neural network (OL-SW-DNN) is proposed and adopted to estimate the action value function. The OL-SW-DNN selects the nearest point data with certain length as training data and is insensitivity to the noise. Finally, the comparison simulations of the proposed method with the proportion-integration-differentiation (PID) that is the most commonly used as an engine controller algorithm in industry are conducted to verify the validity of the proposed method. The results show that, compared with the PID, the acceleration time of the proposed method decreased by 1.525 s under the premise of satisfying all engine limits.