Intelligent direct thrust control for multivariable turbofan engine based on reinforcement and deep learning methods

This paper proposes a novel intelligent direct thrust control (IDTC) architecture for a two-spool turbofan engine, in which the thrust is directly controlled by the IDTC architecture with two control variables instead of transforming thrust command to other commands. To realize thrust control, the unmeasurable thrust should be estimated first. A dynamic thrust model (DTM) is proposed to characterize the strong non-linearity of the engine. With the dynamic characteristic data set generation method, the date diversity increases and the thrust under random engine dynamics can be estimated more accurately. For the multi-variable optimal thrust controller, reinforcement learning (RL) method proximal policy optimization (PPO) algorithm is applied in the intelligent controller (IC) design process. Considering the engine characteristics, the reward system and the controller structure are redesigned to realize thrust optimal control and limit protection. The network used in both the DTM and the IC designing are the long short-term memory (LSTM) recurrent neural network (RNN), which can better process the engine time series and get better performance and accuracy. As the traditional evaluation criteria are too conservative to evaluate the overall ability of the proposed IDTC architecture in the full envelope, new criteria are proposed. Numerical simulations shows the satisfactory thrust estimation and control performance of the proposed IDTC architecture in the full envelope above the idle, and verify the superiority against other methods. Also, the strategy selection of the IC under extreme control tasks also shows the intelligence of the controller and gives new ideas for the engine control law design. (c) 2022 Elsevier Masson SAS. All rights reserved.