基于对抗强化学习的组合动力发动机模态转换智能鲁棒控制方法OA

To address the strong nonlinearity,multi-actuator coupling,and stringent safety and robustness require-ments of combined power engines during modal transition,an intelligent robust control method for the modal 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 modal,multi-input multi-output control strategies and stage-wise training environ-ments are designed for different modal transition processes,enabling adaptive policy learning and robustness improve-ment 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 un-der a centralized training and distributed execution scheme.Furthermore,real-time performance is validated on a hardware-in-the-loop platform,showing that the control cycle meets millisecond-level real-time requirements.Simula-tion results demonstrate that,under typical modal transition conditions,the proposed control method achieves steady-state thrust tracking errors within 1%,while exhibiting superior performance in thrust fluctuation amplitudes during modal transition compared with conventional control approaches.Under observation disturbances and parameter de-viations,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 modal transition of com-bined power engines,providing a feasible solution for intelligent control of wide-speed-range combined power propul-sion systems.