Improved Thrust Performance Optimization Method for UAVs Based on the Adaptive Margin Control Approach

被引:0
|
作者
Wang, Yeguang [1 ,2 ]
Liu, Honglin [3 ]
Liu, Kai [3 ]
机构
[1] Fudan Univ, Dept Aeronaut & Astronaut, Shanghai 200433, Peoples R China
[2] Shenyang Aircraft Design & Res Inst, Shenyang 110034, Peoples R China
[3] Dalian Univ Technol, Sch Aeronaut & Astronaut, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
unmanned aerial vehicle; aircraft; engine integration; thrust optimization; adaptive margin model; adaptive disturbance rejection control;
D O I
10.3390/math11051176
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
This study proposes a strategy for improving the thrust performance of fixed-wing UAV turbine engines from the perspective of aircraft/engine integration. In the UAV engine control process, the inlet distortion caused by the angle of attack change is taken into account, the inlet distortion index is calculated in real time by predicting the angle of attack, and the influence of the inlet distortion on the engine model is analyzed mechanically. Then, the pressure ratio command is adjusted according to the new compressor surge margin requirement caused by the inlet distortion to finally improve the engine thrust performance. To verify the effectiveness of the algorithm, an adaptive disturbance rejection controller is designed for the flight control of a fixed-wing UAV to complete the simulation of horizontal acceleration. The simulation results show that, with this strategy, the UAV turbofan engine can improve the turbofan engine thrust performance by more than 8% under the safety conditions.
引用
收藏
页数:22
相关论文
共 50 条
  • [31] Adaptive Optimization Control Based on Improved Genetic Algorithm and Fuzzy Neural Network
    Pengdong
    Fengdai
    Li, Ningxia
    2009 INTERNATIONAL CONFERENCE ON E-BUSINESS AND INFORMATION SYSTEM SECURITY, VOLS 1 AND 2, 2009, : 1048 - 1051
  • [32] Adaptive VSG parameter control strategy based on improved particle swarm optimization
    Guo J.-Y.
    Fan Y.-P.
    Dianji yu Kongzhi Xuebao/Electric Machines and Control, 2022, 26 (06): : 72 - 82
  • [33] An Adaptive Design Domain Topology Optimization Method Based on Improved Quadtree and SBFEM
    Wang H.
    Wang J.
    Luo H.
    Wang L.
    Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2024, 35 (05): : 904 - 915and927
  • [34] Adaptive improved response surface method for reliability-based design optimization
    Eshghi, Amin Toghi
    Lee, Soobum
    ENGINEERING OPTIMIZATION, 2019, 51 (12) : 2011 - 2029
  • [35] Multivariable adaptive control based consensus flight control system for UAVs formation
    Zhen, Ziyang
    Tao, Gang
    Xu, Yue
    Song, Ge
    AEROSPACE SCIENCE AND TECHNOLOGY, 2019, 93
  • [36] A MODEL-BASED APPROACH TO ADAPTIVE-CONTROL OPTIMIZATION IN MILLING
    WATANABE, T
    JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 1986, 108 (01): : 56 - 64
  • [37] A multiple UAVs path planning method based on model predictive control and improved artificial potential field
    Xian B.
    Song N.
    Kongzhi yu Juece/Control and Decision, 2024, 39 (07): : 2133 - 2141
  • [38] Improving charging performance for wireless rechargeable sensor networks based on charging UAVs: a joint optimization approach
    Li, Songyang
    Wang, Aimin
    Sun, Geng
    Liu, Lingling
    2020 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC), 2020, : 371 - 377
  • [39] A Method of UAVs Route Optimization Based on the Structure of the Highway Network
    Niu, Shuyun
    Zhang, Jisheng
    Zhang, Fan
    Li, Honghai
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2015,
  • [40] Control Surface Optimization of Hypersonic Vehicle Based on Adaptive Backstepping Method
    Wang Cong
    Lu Kunfeng
    PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 3349 - 3354