基于无模型自适应控制的ACE推力保持方法

张玉竺; 周鑫; 陈文娟; 刘鹏飞; 陈前景; 黄金泉; 鲁峰

doi:10.13224/j.cnki.jasp.20230766

基于无模型自适应控制的ACE推力保持方法

doi: 10.13224/j.cnki.jasp.20230766

张玉竺^1,,
周鑫¹,
陈文娟¹,
刘鹏飞¹,
陈前景²,
黄金泉¹,
鲁峰^1,*, ,

1.
南京航空航天大学能源与动力学院，南京 210016
2.
中国航空发动机集团有限公司中国航空发动机研究院，北京 101304

基金项目: 国家自然科学基金（52302472）；航空发动机及燃气轮机基础科学中心项目（P2022-B-Ⅴ-002-001）

详细信息

作者简介:
张玉竺（1999-），女，硕士生，主要从事航空发动机控制算法研究。E-mail：zhang_yz@nuaa.edu.cn

通讯作者:
鲁峰（1981-），男，教授、博士生导师，博士，主要从事航空发动机建模、控制和故障诊断融合方法等研究。E-mail：lufengnuaa@126.com

中图分类号: V235.16
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- 文章访问数: 418
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- 被引次数: 0
出版历程
- 收稿日期: 2023-12-04
- 网络出版日期: 2025-08-12

ACE thrust maintenance method based on model free adaptive control

1.
College of Energy and Power Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 210016，China
2.
Aero Engine Academy of China，Aero Engine Corporation of China，Beijing 101304，China

摘要

摘要:
自适应循环发动机（ACE）具有很强的非线性特征，参数之间耦合严重，难以建立高精度的数学模型。无模型自适应控制（MFAC）算法无需被控对象的精确模型，仅通过对其输入/输出（I/O）数据进行动态线性化就可以实现控制器的设计，计算负担小且鲁棒性较强。以某型自适应循环发动机为研究对象，设计了针对ACE单一模式的多变量无模型自适应控制策略，并通过引入积分环节，减小被控制量的响应波动并改善其动态性能。针对ACE的推力保持控制问题，分别采用人工蜂群（ABC）优化算法和PID-线性规划（PID-LP）综合优化算法设计外环指令修正回路，搭建双环多变量控制系统，以在实现发动机推力保持的同时，控制涡轮温度和压气机喘振裕度不超过限制边界。基于本文所设计的控制结构及策略，面向工程应用开展了硬件在回路仿真试验。仿真结果表明：本文提出的基于改进的MFAC双环推力控制结构能够使发动机获得良好的稳态和动态控制性能，被控制量的波动量均小于2%，且在气路部件性能退化条件下能实现安全限制范围内的推力保持，从而更好地发挥发动机的性能，延长发动机使用寿命，具有工程应用价值。
- 自适应循环发动机 /
- 无模型自适应控制 /
- 推力保持 /
- 优化控制 /
- 多变量控制
Abstract:
Adaptive cycle engine (ACE) has strong nonlinear characteristics and severe coupling between parameters. It is difficult to establish high-precision mathematical models. The model free adaptive control (MFAC) algorithm was employed to design a controller by dynamically linearizing its input/output (I/O) data without an accurate model of the controlled object. This algorithm had low computational burden and strong robustness. A multivariable model free adaptive control strategy for ACE single mode was designed based on a certain adaptive cycle engine. By introducing an integral part, the fluctuation of control variables and the dynamic performance of the system were improved. Regarding the thrust maintenance control problem of ACE, artificial bee colony (ABC) optimization algorithm and PID-linear programming (PID-LP) comprehensive optimization algorithm were proposed to design an outer loop instruction correction loop. A dual loop multivariable control system was constructed to control the turbine temperature and compressor surge margin within the limit boundary while maintaining engine thrust. Hardware in-loop simulation tests were conducted. The simulation results demonstrated that the improved MFAC dual-loop thrust control structure enabled the engine to achieve excellent steady-state and dynamic control performance. The fluctuation of the controlled variable remained below 2%, and thrust maintenance within safe limits can be achieved even under conditions of deteriorating gas path component performance. This approach optimized engine performance, extended engine service life, and exhibited significant engineering application value.
- adaptive cycle engine /
- model free adaptive control /
- thrust maintenance /
- optimization control /
- multivariate control

HTML

图 1 ACE结构示意图

Figure 1. Schematic representation of ACE

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图 2 基于无模型自适应算法的双环多变量控制系统结构图

Figure 2. Structure diagram of a dual loop multivariable control system based on model free adaptive algorithm

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图 3 含积分环节的MFAC结构图

Figure 3. MFAC structure diagram with integral components

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图 4 蜜蜂采蜜过程示意图

Figure 4. Schematic diagram of bee honey picking process

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图 5 基于ABC优化算法的无模型自适应控制系统结构示意图

Figure 5. Schematic diagram of model free adaptive control system structure based on ABC optimization algorithm

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图 6 基于PID-LP综合优化算法的无模型自适应控制系统结构示意图

Figure 6. Schematic diagram of model free adaptive control system structure based on PID-LP comprehensive optimization algorithm

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图 7 HIL仿真试验原理图

Figure 7. HIL simulation tests schematic diagram

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图 8 HIL试验平台组成

Figure 8. Composition of HIL tests platform

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图 9 HIL调试流程

Figure 9. HIL debugging process

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图 10 双外涵模式（M2）仿真结果

Figure 10. Simulation result in double bypass mode （M2）

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图 11 三外涵模式（M3）仿真结果

Figure 11. Simulation result in triple bypass mode （M3）

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图 12 双外涵模式（M2）下变化曲线

Figure 12. Variation curve in double bypass mode （M2）

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图 13 三外涵模式（M3）下变化曲线

Figure 13. Variation curve in triple bypass mode （M3）

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图 14 双外涵模式（M2）下变化曲线（高空点）

Figure 14. Variation curve under double bypass mode （M2）（high-altitude point）

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图 15 HIL仿真结果

Figure 15. HIL simulation results

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图 16 HIL数据处理结果

Figure 16. HIL simulation data processing results

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表 1 ACE典型工作模式

Table 1. Typical operation mode of ACE

模式	第二外涵道	第三外涵道
单外涵模式	关闭	关闭
双外涵模式（M1）	关闭	开启
双外涵模式（M2）	开启	关闭
三外涵模式（M3）	开启	开启

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表 2 ACE各截面编号及定义

Table 2. Number and definition of each section of ACE

截面编号	截面定义
1	进气道与发动机的交界面
2	可调风扇进口截面
25	高压压气机进口截面
3	燃烧室进口截面
4	高压涡轮进口截面
5	低压涡轮进口截面
6	混合燃烧室进口截面
7	尾喷管进口截面
8	尾喷管喉道截面
9	尾喷管出口截面

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参考文献(31)

[1]	杨宇飞. 自适应循环发动机建模及控制规律研究[D]. 南京: 南京航空航天大学, 2017. YANG Yufei. Research on modeling and control law of adaptive cycle engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017. (in Chinese YANG Yufei. Research on modeling and control law of adaptive cycle engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2017. (in Chinese)
[2]	ZHANG Jiyuan, TANG Hailong, CHEN Min. Robust design of an adaptive cycle engine performance under component performance uncertainty[J]. Aerospace Science and Technology, 2021, 113: 106704. doi: 10.1016/j.ast.2021.106704
[3]	刘晓锋. 基于混合灵敏度的航空发动机切换控制系统设计[J]. 航空动力学报, 2011, 26(6): 1389-1395. LIU Xiaofeng. Switching control system design for aero-engine based on mixed sensitivity[J]. Journal of Aerospace Power, 2011, 26(6): 1389-1395. (in Chinese LIU Xiaofeng. Switching control system design for aero-engine based on mixed sensitivity[J]. Journal of Aerospace Power, 2011, 26(6): 1389-1395. (in Chinese)
[4]	侯忠生. 非参数模型及其自适应控制理论[M]. 北京: 科学出版社, 1999. HOU Zhongsheng. Nonparametric model and its adaptive control theory[M]. Beijing: Science Press, 1999. (in Chinese HOU Zhongsheng. Nonparametric model and its adaptive control theory[M]. Beijing: Science Press, 1999. (in Chinese)
[5]	陈芊, 盛汉霖, 张天宏. 一种商用大涵道比涡扇发动机无模型自适应性能优化控制方法[J]. 推进技术, 2023, 44(12): 2210030. CHEN Qian, SHENG Hanlin, ZHANG Tianhong. A model-free adaptive performance optimization control method for high-bypass ratio commercial turbofan engine[J]. Journal of Propulsion Technology, 2023, 44(12): 2210030. (in Chinese CHEN Qian, SHENG Hanlin, ZHANG Tianhong. A model-free adaptive performance optimization control method for high-bypass ratio commercial turbofan engine[J]. Journal of Propulsion Technology, 2023, 44(12): 2210030. (in Chinese)
[6]	陈敏, 张纪元, 唐海龙, 等. 自适应循环发动机总体设计技术探讨[J]. 航空动力学报, 2022, 37(10): 2046-2058. CHEN Min, ZHANG Jiyuan, TANG Hailong, et al. Discussion on overall performance design technology of adaptive cycle engine[J]. Journal of Aerospace Power, 2022, 37(10): 2046-2058. (in Chinese CHEN Min, ZHANG Jiyuan, TANG Hailong, et al. Discussion on overall performance design technology of adaptive cycle engine[J]. Journal of Aerospace Power, 2022, 37(10): 2046-2058. (in Chinese)
[7]	LIST M G. Adaptive-cycle engine design continues to mature[J]. Aerospace America, 2015, 53(11): 52-52.
[8]	LYU Ya, TANG Hailong, CHEN Min. A study on combined variable geometries regulation of adaptive cycle engine during throttling[J]. Applied Sciences, 2016, 6(12): 374. doi: 10.3390/app6120374
[9]	KIRA K, RENDELL L A. The feature selection problem: Traditional methods and a new algorithm: Proceedings of the tenth national conference on Artificial intelligence[C]//Proceedings of the 10the national conference on artificial intelligence. San Jose, US, AAAI Press, 1992: 129-134.
[10]	管庭筠. 涡扇发动机直接性能量控制综合[D]. 南京: 南京航空航天大学, 2020. GUAN Tingjun. Synthesis of direct energy control for turbofan engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020. (in Chinese GUAN Tingjun. Synthesis of direct energy control for turbofan engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2020. (in Chinese)
[11]	史东烨, 缑林峰. 航空发动机无模型自适应生物智能控制方法研究[J]. 计算机测量与控制, 2016, 24(10): 104-107. SHI Dongye, GOU Linfeng. Aero-engine model free adaptive intelligent control research[J]. Computer Measurement & Control, 2016, 24(10): 104-107. (in Chinese SHI Dongye, GOU Linfeng. Aero-engine model free adaptive intelligent control research[J]. Computer Measurement & Control, 2016, 24(10): 104-107. (in Chinese)
[12]	SONG Weizhao, FENG Jian, LIU Jinze. Distributed MIMO MFAC-based consensus tracking strategy for multiagent systems with fixed and switching topologies[J]. International Journal of Systems Science, 2022, 53(9): 1888-1905. doi: 10.1080/00207721.2022.2031336
[13]	JIANG Gaoyang, LIU Genfeng, YU Hansong. A model free adaptive scheme for integrated control of civil aircraft trajectory and attitude[J]. Symmetry, 2021, 13(2): 347. doi: 10.3390/sym13020347
[14]	王卫红, 侯忠生, 霍海波, 等. 一类基于数据驱动的控制器设计及其参数整定方法[C]//第29届中国控制会议论文集. 北京: 北京航空航天大学出版社, 2010: 5090-5095. WANG Weihong, HOU Zhongsheng, HUO Haibo, et al. A class of data-driven based controller design and its parameters tuning method[C]// Proceedings of the 29th Chinese Control Conference. Beijing: Beijing University of Aeronautics and Astronautics Press, 2010: 5090-5095. (in Chinese WANG Weihong, HOU Zhongsheng, HUO Haibo, et al. A class of data-driven based controller design and its parameters tuning method[C]// Proceedings of the 29th Chinese Control Conference. Beijing: Beijing University of Aeronautics and Astronautics Press, 2010: 5090-5095. (in Chinese)
[15]	HOU Zhongsheng, XIONG Shuangshuang. On model-free adaptive control and its stability analysis[J]. IEEE Transactions on Automatic Control, 2019, 64(11): 4555-4569. doi: 10.1109/TAC.2019.2894586
[16]	邓望权, 田震, 王子楠, 等. 基于PI与无模型自适应控制结合的燃气轮机转速控制方法[J]. 推进技术, 2022, 43(7): 210006. DENG Wangquan, TIAN Zhen, WANG Zinan, et al. Speed control method of gas turbine based on combination of PI and model free adaptive control[J]. Journal of Propulsion Technology, 2022, 43(7): 210006. (in Chinese DENG Wangquan, TIAN Zhen, WANG Zinan, et al. Speed control method of gas turbine based on combination of PI and model free adaptive control[J]. Journal of Propulsion Technology, 2022, 43(7): 210006. (in Chinese)
[17]	WAKITANI S, NAKANISHI H, ASHIDA Y, et al. Study on a Kalman filter based PID controller[J]. IFAC-PapersOnLine, 2018, 51(4): 422-425. doi: 10.1016/j.ifacol.2018.06.131
[18]	杨思幸. 航空发动机性能恢复控制方法研究[D]. 南京: 南京航空航天大学, 2019. YANG Sixing. Research on performance recovery control method of aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019. (in Chinese YANG Sixing. Research on performance recovery control method of aero-engine[D]. Nanjing: Nanjing University of Aeronautics and Astronautics, 2019. (in Chinese)
[19]	ZHOU Xin, HUANG Jinquan, LU Feng, et al. HNN-based generalized predictive control for turbofan engine direct performance optimization[J]. Aerospace Science and Technology, 2021, 112: 106602. doi: 10.1016/j.ast.2021.106602
[20]	LI L, GAO Y M, WU Z H, et al. Small fault detection for satellite attitude control system actuators with stacked autoencoder network[J]. IOP Conference Series: Materials Science and Engineering, 2018, 449: 012016. doi: 10.1088/1757-899X/449/1/012016
[21]	刘尚明, 何皑, 郭森闯. 基于神经网络的燃气轮机控制和建模技术综述[J]. 热力透平, 2022, 51(3): 155-160, 169. LIU Shangming, HE Ai, GUO Senchuang. Review of gas turbine control and modeling technology based on neural network[J]. Thermal Turbine, 2022, 51(3): 155-160, 169. (in Chinese LIU Shangming, HE Ai, GUO Senchuang. Review of gas turbine control and modeling technology based on neural network[J]. Thermal Turbine, 2022, 51(3): 155-160, 169. (in Chinese)
[22]	黄金泉, 仇小杰, 鲁峰, 等. 航空发动机性能恢复控制方法[J]. 航空动力学报, 2012, 27(7): 1595-1603. HUANG Jinquan, QIU Xiaojie, LU Feng, et al. Performance recovery control method for aircraft engine[J]. Journal of Aerospace Power, 2012, 27(7): 1595-1603. (in Chinese HUANG Jinquan, QIU Xiaojie, LU Feng, et al. Performance recovery control method for aircraft engine[J]. Journal of Aerospace Power, 2012, 27(7): 1595-1603. (in Chinese)
[23]	王曦. 涡扇发动机自适应加减速控制研究[J]. 航空发动机, 2021, 47(2): 1-6. WANG Xi. Research on adaptive acceleration and deceleration control of turbofan engine[J]. Aeroengine, 2021, 47(2): 1-6. (in Chinese WANG Xi. Research on adaptive acceleration and deceleration control of turbofan engine[J]. Aeroengine, 2021, 47(2): 1-6. (in Chinese)
[24]	王曦, 李志鹏. 涡扇发动机极值限制保护闭环控制设计[J]. 航空发动机, 2016, 42(5): 1-7. WANG Xi, LI Zhipeng. Design of limit protection controller with closed loop for turbofan engine[J]. Aeroengine, 2016, 42(5): 1-7. (in Chinese WANG Xi, LI Zhipeng. Design of limit protection controller with closed loop for turbofan engine[J]. Aeroengine, 2016, 42(5): 1-7. (in Chinese)
[25]	孙瑞谦, 缑林峰, 韩小宝, 等. 考虑性能退化的航空发动机故障诊断量化评估[J]. 推进技术, 2022, 43(8): 210247. SUN Ruiqian, GOU Linfeng, HAN Xiaobao, et al. Quantitative evaluation of fault diagnosability for degraded aero-engine[J]. Journal of Propulsion Technology, 2022, 43(8): 210247. (in Chinese SUN Ruiqian, GOU Linfeng, HAN Xiaobao, et al. Quantitative evaluation of fault diagnosability for degraded aero-engine[J]. Journal of Propulsion Technology, 2022, 43(8): 210247. (in Chinese)
[26]	LIU Xiaofeng, CHEN Yingjie, XIONG Liuqi, et al. Intelligent fault diagnosis methods toward gas turbine: a review[J]. Chinese Journal of Aeronautics, 2024, 37(4): 93-120. doi: 10.1016/j.cja.2023.09.024
[27]	陈宇寒, 肖玲斐, 卢彬彬. 融合蜂群优化航空发动机自适应PID控制[J]. 控制工程, 2019, 26(2): 229-235. CHEN Yuhan, XIAO Lingfei, LU Binbin. Aeroengine adaptive PID control based on hybrid artificial bee colony algorithm[J]. Control Engineering of China, 2019, 26(2): 229-235. (in Chinese CHEN Yuhan, XIAO Lingfei, LU Binbin. Aeroengine adaptive PID control based on hybrid artificial bee colony algorithm[J]. Control Engineering of China, 2019, 26(2): 229-235. (in Chinese)
[28]	GARCÍA NIETO P, GARCÍA-GONZALO E, BERNARDO SÁNCHEZ A, et al. A new predictive model based on the ABC optimized multivariate adaptive regression splines approach for predicting the remaining useful life in aircraft engines[J]. Energies, 2016, 9(6): 409. doi: 10.3390/en9060409
[29]	许国根, 赵后随, 黄智勇. 最优化方法及其MATLAB实现[M]. 1版. 北京: 北京航空航天大学出版社, 2018. XU Guogen, ZHAO Housui, HUANG Zhiyong. Optimization method and its MATLAB implementation[M]. 1st ed. Beijing: Beijing University of Aeronautics & Astronautics Press, 2018. (in Chinese XU Guogen, ZHAO Housui, HUANG Zhiyong. Optimization method and its MATLAB implementation[M]. 1st ed. Beijing: Beijing University of Aeronautics & Astronautics Press, 2018. (in Chinese)
[30]	李明, 宋志平, 刘一龙, 等. 航空发动机控制系统容错验证平台设计及试验方法研究[J]. 实验技术与管理, 2022, 39(6): 97-103. LI Ming, SONG Zhiping, LIU Yilong, et al. Design and test method research of fault tolerance verification platform for aero engine control system[J]. Experimental Technology and Management, 2022, 39(6): 97-103. (in Chinese LI Ming, SONG Zhiping, LIU Yilong, et al. Design and test method research of fault tolerance verification platform for aero engine control system[J]. Experimental Technology and Management, 2022, 39(6): 97-103. (in Chinese)
[31]	杨征山, 仇小杰, 庄锡明, 等. 基于模态切换的航空发动机容错控制[J]. 航空动力学报, 2014, 29(4): 953-964. YANG Zhengshan, QIU Xiaojie, ZHUANG Ximing, et al. Aero-engine fault-tolerant control based on mode switch[J]. Journal of Aerospace Power, 2014, 29(4): 953-964. (in Chinese YANG Zhengshan, QIU Xiaojie, ZHUANG Ximing, et al. Aero-engine fault-tolerant control based on mode switch[J]. Journal of Aerospace Power, 2014, 29(4): 953-964. (in Chinese)