ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Improved DDPG-based multipoint pressure distribution control of variable geometry scramjet combustor at wide range velocities
Received date: 2024-08-23
Revised date: 2024-09-19
Accepted date: 2024-10-24
Online published: 2024-11-07
Supported by
National Key Laboratory Project on Ramjet Engine Technology of China(WDZC6142703202402)
Generally， the variable geometry combustor can ensure suitable performance of scramjets. However， its multivariate and strong coupling characteristics make it difficult to develop high-performance control algorithms， and also become an intractable problem hindering the development of the scramjet field. To solve the contradiction between control design and mechanical coupling， this paper firstly proposes a multipoint fuel injection structure for the conventional variable geometry combustor. The newly proposed structure can provide more operable inputs， thereby ensuring a theoretical ability to improve the system performance. On the basis of the multipoint fuel injection structure， a new LSTM/DDPG-based control scheme is proposed to solve the problem of distributed pressure control in the case of supersonic combustion. This scheme can effectively improve the potential performance of the scramjet at wide range velocities and in long flight time. The effectiveness of the proposed scheme is then verified with the simulation results， i.e.， the LSTM/DDPG-based control scheme can significantly optimize the combustion performance of variable geometry combustor under different operating conditions. Finally， the applicability of the proposed scheme is validated on the Hardware-In-the-Loop （HIL） simulation platform， and the pressure distribution can be effectively controlled at different expansion ratios. The high-accuracy control of multipoint pressure distribution at wide range velocities for the variable geometry combustor is shown to be realized.
Wenhui LING , Chunhui MU , Lingcong NIE , Xian DU , Ximing SUN . Improved DDPG-based multipoint pressure distribution control of variable geometry scramjet combustor at wide range velocities[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(12) : 131092 -131092 . DOI: 10.7527/S1000-6893.2024.31092
| [1] | 刘小勇， 王明福， 刘建文， 等. 超燃冲压发动机研究回顾与展望［J］. 航空学报， 2024， 45（5）： 529878. |
| LIU X Y， WANG M F， LIU J W， et al. Review and prospect of research on scramjet［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（5）： 529878 （in Chinese）. | |
| [2] | JI C W， WU S F， YI Y， et al. Effects of hydrogen injection strategies on the flow field and combustion characteristics in a hydrogen-fueled rotary engine with the swirl chamber［J］. Fuel， 2024， 364： 130951. |
| [3] | 王璐， 钱战森， 高亮杰. 基于边界层燃烧方法的宽速域飞行器内流道减阻研究［J］. 推进技术， 2022， 43（6）： 142-151. |
| WANG L， QIAN Z S， GAO L J. Drag reduction by boundary layer combustion on internal flowpath in a wide-range Mach numbers vehicle?［J］. Journal of Propulsion Technology， 2022， 43（6）： 142-151 （in Chinese）. | |
| [4] | HU R J， WANG W Z. Numerical study of pipeline distribution effect on the performance of pasty propellant rocket motor［J］. Space： Science and Technology， 2023， 3： 83. |
| [5] | 赵李北， 夏智勋， 马立坤， 等. 固体火箭超燃冲压发动机燃烧室构型对燃烧特性影响研究［J］. 空天防御， 2024， 7（3）： 54-63. |
| ZHAO L B， XIA Z X， MA L K， et al. Study on the influence of combustion chamber configuration on combustion characteristics of solid rocket scramjet?［J］. Air & Space DEFENSE， 2024， 7（3）： 54-63 （in Chinese）. | |
| [6] | 曹瑞峰， 崔涛， 于达仁. 两点燃料注入超声速燃烧压力分布优化控制方法［C］∥高超声速专题研讨会暨第五届全国高超声速科学技术会议论文集. 北京： 中国力学学会， 2012. |
| CAO R F， CUI T， YU D R. Optimal control of pressure distribution for two-point fuel injection supersonic combustion［C］∥Proceedings of the Hypersonic Symposium and the 5th National Conference on Hypersonic Science and Technology. Beijing： Chinese Society of Mechanics， 2012 （in Chinese）. | |
| [7] | 杨庆春. 宽速域超声速燃烧室的热力与几何调节方法研究［D］. 哈尔滨： 哈尔滨工业大学， 2015： 22-35. |
| YANG Q C. The research on thermal and geometric adjustment methods of wide-speed supersonic combustion chamber［D］. Harbin： Harbin Institute of Technology， 2015：22-35 （in Chinese）. | |
| [8] | 杜宪， 马艳华， 王欣悦， 等. 基于神经网络时延预测的航空发动机内模控制器设计（英文）［J］. 推进技术， 2023， 44（11）： 219-231. |
| DU X， MA Y H， WANG X Y， et al. Design of aero-engine internal model control system based on neural network time-delay prediction?［J］. Journal of Propulsion Technology. 2023； 44（11）： 219-231. | |
| [9] | MNIH V， KAVUKCUOGLU K， SILVER D， et al. Human-level control through deep reinforcement learning［J］. Nature， 2015， 518（7540）： 529-533. |
| [10] | 肖冰， 张海朝. 航天器姿态稳定强化学习鲁棒最优控制方法［J］. 航空学报， 2024， 45（1）： 628890. |
| XIAO B， ZHANG H C. Reinforcement learning robust optimal control for spacecraft attitude stabilization［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（1）： 628890 （in Chinese）. | |
| [11] | 盛汉霖， 顾至诚， 陈芊， 等. 嵌入式分布式架构下航空发动机模型预测控制方法研究［J］. 推进技术， 2023， 44（11）： 203-218. |
| SHENG H L， GU Z C， CHEN Q， et al. Research and implementation of model predictive control for aircraft engines with embedded distributed architecture［J］. Journal of Propulsion Technology， 2023， 44（11）： 203-218 （in Chinese）. | |
| [12] | LIU Q Q， ZHANG H X， ZHANG X， et al. Improved DDPG based two-timescale multi-dimensional resource allocation for multi-access edge computing networks［J］. IEEE Transactions on Vehicular Technology， 2024， 73（6）： 9153-9158. |
| [13] | ZHU M， TIAN K， WEN Y Q， et al. Improved PER-DDPG based nonparametric modeling of ship dynamics with uncertainty?［J］. Ocean Engineering， 2023， 286： 115513. |
| [14] | 金哲豪， 刘安东， 俞立. 基于GPR和深度强化学习的分层人机协作控制［J］. 自动化学报， 2022， 48（9）： 2352-2360. |
| JIN Z H， LIU A D， YU L. Hierarchical human-robot cooperative control based on GPR and deep reinforcement learning?［J］. Acta Automatica Sinica， 2022， 48（9）： 2352-2360 （in Chinese）. | |
| [15] | WU H， SONG S J， YOU K Y， et al. Depth control of model-free AUVs via reinforcement learning［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2019， 49（12）： 2499-2510. |
| [16] | 丰硕. 宽速域冲压发动机几何可调燃烧室性能影响规律研究［D］. 哈尔滨： 哈尔滨工业大学， 2017： 21-29. |
| FENG S. Study on the influence law of geometric adjustable combustion chamber performance of ramjet in wide speed range［D］. Harbin： Harbin Institute of Technology， 2017： 21-29 （in Chinese）. | |
| [17] | JI M D， XU G J， DUAN Z K， et al. Cooperative pursuit with multiple pursuers based on deep minimax Q-learning［J］. Aerospace Science and Technology， 2024， 146： 108919. |
| [18] | MOUSA A. Extended-deep Q-network： A functional reinforcement learning-based energy management strategy for plug-in hybrid electric vehicles［J］. Engineering Science and Technology， an International Journal， 2023， 43： 101434. |
| [19] | PARK J W， HARLEY R G， VENAYAGAMOORTHY G K. Adaptive-critic-based optimal neurocontrol for synchronous generators in a power system using MLP/RBF neural networks［J］. IEEE Transactions on Industry Applications， 2003， 39（5）： 1529-1540. |
| [20] | JIN H， QI H， ZHAO J， et al. Software systems implementation and domain-specific architectures towards graph analytics?［J］. Intelligent Computing， 2022： 9806758. |
| [21] | HOU Y N， LIU L F， WEI Q， et al. A novel DDPG method with prioritized experience replay?［C］?∥2017 IEEE International Conference on Systems， Man， and Cybernetics （SMC）. Piscataway： IEEE Press， 2017. |
| [22] | SHI D Y， GAN W S， SHEN X Y， et al. What is behind the meta-learning initialization of adaptive filter？—A naive method for accelerating convergence of adaptive multichannel active noise control［J］. Neural Networks， 2024， 172： 106145. |
/
| 〈 |
|
〉 |
CJA