Northwestern Polytechnical University
Supported by Advanced Jet Propulsion Creativity Center,AEAC (Project ID.HKCX2020-02-019)
With the development of artificial intelligence technology, intelligent aircraft engines have gradually become a hot spot in the field of aviation today. Traditional aero-engine control heavily relies on the engine model, and the theoretical modeling approach based on aerothermodynamic formula introduces modeling error that may degrade the performance of controller. This paper proposes a virtual self-learning approah for aero-engine intelligent controller design. Firstly, a virtual environment is established from the testing data of the aero-engine via LSTM neural network; Secondly, the reinforcement learning algorithm based on TD3 is employed for intelligent controller training in the virtual environment, Finally, the JT9D aero-engine model is utilized for controller performance evaluation. The simulation comparisons between intelligent controller and traditional PID control show that the intelligent controller has remarkable performance due to the less overshoot and shorter setting time.