Abstract
This study proposes an active surge control method based on deep reinforcement learn-ing to ensure the stability of compressors when adhering to the pressure rise command across the wide operating range of an aeroengine.Initially,the study establishes the compressor dynamic model with uncertainties,disturbances,and Close-Coupled Valve(CCV)actuator delay.Building upon this foundation,a Partially Observable Markov Decision Process(POMDP)is defined to facilitate active surge control.To address the issue of unobservability,a nonlinear state observer is designed using a finite-time high-order sliding mode.Furthermore,an Improved Soft Actor-Critic(ISAC)algorithm is developed,incorporating prioritized experience replay and adaptive tem-perature parameter techniques,to strike a balance between exploration and convergence during training.In addition,reasonable observation variables,error-segmented reward functions,and ran-dom initialization of model parameters are employed to enhance the robustness and generalization capability.Finally,to assess the effectiveness of the proposed method,numerical simulations are conducted,and it is compared with the fuzzy adaptive backstepping method and Second-Order Sliding Mode Control(SOSMC)method.The simulation results demonstrate that the deep rein-forcement learning based controller outperforms other methods in both tracking accuracy and robustness.Consequently,the proposed active surge controller can effectively ensure stable opera-tion of compressors in the high-pressure-ratio and high-efficiency region.
基金项目
National Natural Science Foundation of China(51976089)
Science Center for Gas Turbine Project,China(P2023-B-V-001-001)
China Scholarship Council(202306830092)
NETL
NSTL
万方数据