Calculation method for air data based on information from inertial navigation system and flight control system

被引：0
|
作者：
Lu C. [1 ]
Li R.-B. [1 ]
Liu J.-Y. [1 ]
Lei T.-W. [2 ]
Guo Y. [2 ]
机构：
[1] Jiangsu Key Laboratory of Internet of Things and Control Technologies, College of Automatic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Chengdu Aircraft Design and Research Institute, Chengdu
来源：
Kongzhi yu Juece/Control and Decision | 2017年 / 32卷 / 02期
关键词：
Aerodynamic model; Air data; EKF; INS;
D O I：
10.13195/j.kzyjc.2015.1418
中图分类号：
学科分类号：
摘要：
The flush air data system(FADS) gets lowaccuracy during high altitude flight, which can easily fail if the vehicle travel through the atmosphere. Therefore, a calculation method for air data based on inertial navigation system(INS) and flight control system(FCS) is proposed. On the base of the aerodynamic model, force equations, and moment equations, the mathematic relationship between INS and air data is established. Real-time estimation of air data is achieved by using EKF. The simulation results show that, this method not only has high precision, favorable stability and robustness, but also improves the measurement range and reliability of the air data system, which can effectively be used to measure the angle of attack, the angle of sideslip and true airspeed within full flight envelope. © 2017, Editorial Office of Control and Decision. All right reserved.
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页码：363 / 367
页数：4
相关论文
共 18 条
  • [1] Wang Y.F., Wu Q.X., Jiang C.S., Et al., Multi-model switch-ing control for near space vehicle, Control and Decision, 27, 10, pp. 1452-1458, (2012)
  • [2] Guo J.F., Fu Y., Cui N.G., Three dimensional autonomous entry guidance method, Control and Decision, 28, 5, pp. 688-694, (2013)
  • [3] Yadav V., Padhi R., Balakrishnan S.N., Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks, IEEE Trans on Neural Networks, 18, 4, pp. 1115-1128, (2007)
  • [4] Adhika Pradipta Lie F., Gebre-Egziabher D., Synthetic air data system, J of Aircraft, 50, 4, pp. 1234-1249, (2013)
  • [5] Rohlf D., Brieger O., Grohs T., X-31 VECTOR system identification-approach and results, AIAA Atmospheric Flight Mechanics Conf., pp. 4830-4842, (2004)
  • [6] Baumann E., Pahle J.W., Davis M.C., Et al., X-43A flush air data sensing system flight-test results, J of Spacecraft and Rockets, 47, 1, pp. 48-61, (2010)
  • [7] Li Q.D., Chen L.L., Zhang X.G., Et al., Flush airdata sensing system fast intelligent fault detection and diagnosis technology, Systems Engineering and Electronics, 31, 10, pp. 2544-2546, (2009)
  • [8] Whitemore S.A., Reconstruction of the shuttle reentry air data parameters using a linearized Kalman filter, AIAA Atmospheric Flight Mechanics Conf., pp. 2097-2110, (1983)
  • [9] Wise K.A., Flight testing of the X-45A J-UCAS computational Alpha-Beta system, AIAA Guidance, Navigation, and Control Conf., pp. 6215-6229, (2006)
  • [10] Rhudy M., Larrabee T., Chao H., Et al., UAV attitude, heading, and wind estimation using GPS/INS and an air data system, AIAA Guidance, Navigation, and Control Conf., pp. 5201-5212, (2013)
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