Design of Intelligent Controller for Aero-engine Based on TD3 Algorithm

Abstract

Recently, higher structure complicacy and performances requirements of the aero-engine have brought higher demands on its control system. For the control of aerodynamic thermodynamic system, the intelligent control method with self-learning ability will be a promising choice. In the paper, we propose an aero-engine intelligent controller design method based on twin delayed deep deterministic policy gradient (TD3) algorithm. The method enables the intelligent controller to learn continuously according to the feedback of the environment and control the aero-engine. The paper takes the intelligent controller design of the JT9D turbofan engine as an example. First, the aero-engine control problem is described as a Markov decision process for deep reinforcement learning algorithms. Second, a complete intelligent controller design process is constructed by reasonably designing the network structure and reward function. Finally, the comparison simulations are conducted to verify the effectiveness of the proposed methods. The simulation results show that the TD3 controller outperforms deep deterministic policy gradient (DDPG) and the proportional-integral-derivative (PID) in the aero-engine control task. And the TD3 controller can realize the tracking control of low-pressure turbine speed with quick response and small overshoot.