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

Recently, higher structure complicacy and performance requirements of the aero-engine have brought higher demands on its control system. With the rapid development of artificial intelligence technology, the intelligent controller with self-learning ability will be able to make a great difference. In the paper, we propose an aero-engine intelligent controller design method based on twin delayed deep deterministic policy gradient (TD3) algorithm. The design method allows the intelligent controller to interact autonomously with the aero-engine system to acquire the optimal control sequence. The JT9D turbofan engine is used to introduce the controller design workflow proposed in the paper. First, the problem of aero-engine control is described as a Markov decision process for deep reinforcement learning (DRL) algorithms. Second, a complete intelligent controller design process is constructed by reasonably designing the network structures and reward function. Finally, the comparison simulations are carried out to verify the superior performance of the controller design methods. The simulation results indicate that low-pressure turbine speed has no overshoot, and the settling time does not exceed 0.88s during the engine acceleration process. In the deceleration process, the overshoot of the low-pressure turbine speed is limited to 0.74% and the settling time does not exceed about 0.6s. The results prove that the TD3 controller outperforms deep deterministic policy gradient (DDPG) and the proportional-integral-derivative (PID) in the speed tracking control.