关键词: 组合动力发动机, 模态转换, 智能鲁棒控制, 强化学习, 对抗训练

Abstract: To address the strong nonlinearity, multi-actuator coupling, and stringent safety and robustness requirements of combined power engines during mode transition, an intelligent robust control method for the mode transition process is investigated. Focusing on the coordinated satisfaction of thrust tracking performance and safety constraints, an intelligent control framework based on deep reinforcement learning is established, in which an adversarial training mechanism is introduced to enhance robustness against observation disturbances and uncertainties. Based on a component-level engine simulation model, multi-input multi-output control strategies and stage-wise training environments are designed for different mode transition processes, enabling adaptive policy learning and robustness improvement through adversarial reinforcement learning. In addition, to cope with engine parameter variations, a distributed control architecture based on multi-agent reinforcement learning is developed, and controller training is carried out under a centralized training and distributed execution scheme. Simulation results demonstrate that, under typical mode transition conditions, the proposed control method achieves steady-state thrust tracking errors within 1%, while reducing thrust fluctuation amplitudes during mode transition by approximately 30% compared with conventional control approaches. Under observation disturbances and parameter deviations, safety constraints are consistently satisfied without violation. The results indicate that the proposed intelligent robust control method effectively improves control accuracy, safety, and robustness during mode transition of combined power engines, providing a feasible solution for intelligent control of wide-speed-range combined power propulsion systems.

Key words: combined power engine, mode transition, intelligent robust control, reinforcement learning, adversarial training