Optimization control with multi-constraint of aeroengine acceleration process based on reinforcement learning

Juan Fang; Qiangang Zheng; Weimin Liu; Haibo Zhang

doi:10.1117/12.2671152

28 April 2023 Optimization control with multi-constraint of aeroengine acceleration process based on reinforcement learning

Juan Fang, Qiangang Zheng, Weimin Liu, Haibo Zhang

Author Affiliations +

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126105N (2023) https://doi.org/10.1117/12.2671152
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

With the development of Reinforcement Learning (RL), it becomes able to solve the continuous action space problem and shows strong ability in dealing with complex nonlinear control problem. Based on the Deep Deterministic Policy Gradient (DDPG) algorithm, a novel scheme of aeroengine acceleration controller is proposed in this paper. According to the characteristics of the engine acceleration stage, the reward function is constructed, and the state parameters are updated in the form of sliding window to reduce the sensitivity of the network to noise. DDPG adopts actor-critic framework, critic calculates value function by the deep neural network, actor outputs action command and forms a closed-loop control system with the engine. The method is verified by digital simulation at ground condition and the results demonstrate that compared with the traditional PID controller, the acceleration time of DDPG controller is reduced by 41.56%. Additionally, the network converges within 400 steps.

Citation Download Citation

Juan Fang, Qiangang Zheng, Weimin Liu, and Haibo Zhang "Optimization control with multi-constraint of aeroengine acceleration process based on reinforcement learning", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126105N (28 April 2023); https://doi.org/10.1117/12.2671152

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