Journal Article

Aero-Engine Modeling and Control Method with Model-Based Deep Reinforcement Learning

Gao, Wenbo, Huang, Jin-Quan, Zhou, Wenxiang, Lu, Feng, Pan, Muxuan

2023

Overview

Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning (DRL) is presented for aero-engine control. In addition, transition dynamic characteristic information of the aero-engine is extracted from the replay buffer of deep reinforcement learning to train a neural-network dynamic prediction model for the aero-engine. In turn, the dynamic prediction model is used to improve the learning efficiency of reinforcement learning. The practical applicability of the proposed control system is demonstrated by the numerical simulations. Compared with the traditional control system, this novel aero-engine control system has faster response speed, stronger self-learning ability, and avoids the complicated manual parameter adjustment without sacrificing the control performance. Moreover, the dynamic prediction model has satisfactory prediction accuracy, and the model-based method can achieve higher learning efficiency than the model-free method.

• Publisher: MDPI AG
• Subject: aero-engine control / Aerospace engines / Artificial intelligence / Control methods / Control systems / Controllers / Deep learning / Dynamic characteristics / Efficiency / Energy consumption / Engine control / Engines / Machine learning / Mathematical models / Methods / Model accuracy / Modelling / neural network / Neural networks / nonlinear system / Nonlinear systems / Numerical analysis / Numerical simulations / Optimization algorithms / Prediction models / Probability distribution / reinforcement learning / Simulation methods / Variables