Reentry guidance algorithm based on Kalman filter for aerospace vehicles

YOU Zhipeng; YANG Yong; LIU Gang; CAO Xiaorui; ZHENG Hongtao

doi:10.7527/S1000-6893.2020.24608

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 11: 524608 - 524608

DOI: https://doi.org/10.7527/S1000-6893.2020.24608

Article

Reentry guidance algorithm based on Kalman filter for aerospace vehicles

YOU Zhipeng ,
YANG Yong ,
LIU Gang ,
CAO Xiaorui ,
ZHENG Hongtao

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China Academy of Launch Vehicle Technology, Beijing 100076, China

Received date: 2020-08-05

Revised date: 2020-08-21

Online published: 2020-10-23

Supported by

Defense Industrial Technology Development Program(JCKY2019203A003)

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Abstract

A new predictor-corrector reentry guidance algorithm based on Kalman filter is proposed to improve the real-time performance of predictor-corrector reentry guidance for aerospace vehicles. The new algorithm fits the velocity-altitude flight profile with the fourth-order polynomial. To compute the fitting coefficients satisfying the requirements of the reentry corridor and the range to be flown, the altitude corresponding to the selected speed point is estimated by Kalman filter. The reentry flight time is adjusted by reducing a terminal constraint and adding a profile parameter to be estimated in the algorithm. It is found that the adaptability of guidance instructions can be improved by correcting uncertain parameters through online identification during the reentry. At the end of the flight, the divergence of the fitting coefficients when the flight speed is close to the terminal speed can be avoided by tracking the reference profile. The simulation results of different reentry conditions demonstrate that the reentry guidance algorithm based on Kalman filter has better real-time performance, higher guidance accuracy, controllable flight time, stronger robustness and engineering application potential.

Key words： aerospace vehicles; predictor-corrector guidance; velocity-height profile; Kalman filter; online identification

Cite this article

YOU Zhipeng , YANG Yong , LIU Gang , CAO Xiaorui , ZHENG Hongtao . Reentry guidance algorithm based on Kalman filter for aerospace vehicles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(11) : 524608 -524608 . DOI: 10.7527/S1000-6893.2020.24608

References

[1] GRANTZ A. X-37B orbital test vehicle and derivatives[C]//AIAA SPACE 2011 Conference & Exposition. Reston:AIAA, 2011:7315.
[2] SUCCA M, BOSCOLO I, DROCCO A, et al. IXV avionics architecture:Design, qualification and mission results[J]. Acta Astronautica, 2016, 124:67-78.
[3] 杨勇, 张辉, 郑宏涛. 有翼高超声速再入飞行器气动设计难点问题[J]. 航空学报, 2015, 36(1):49-57. YANG Y, ZHANG H, ZHENG H T. Difficulties inaerodynamic design problems of the winged hypersonic reentry vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(1):49-57(in Chinese).
[4] 张远龙, 谢愈. 滑翔飞行器弹道规划与制导方法综述[J]. 航空学报, 2020, 41(1):023377. ZHANG Y L, XIE Y. Review of trajectory planning and guidance methods for gliding vehicles[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(1):023377(in Chinese).
[5] POWELL R. Numerical roll reversal predictor corrector aerocapture and precision landingguidance algorithms for the Mars Surveyor Program 2001 missions[C]//23rd Atmospheric Flight Mechanics Conference. Reston:AIAA, 1998:4574.
[6] FUHRY D. Adaptive atmospheric reentry guidance for the Kistler K-1 orbital vehicle[C]//Guidance, Navigation, and Control Conference and Exhibit. Reston:AIAA, 1999:4211.
[7] LU P. Predictor-corrector entry guidance for low-lifting vehicles[J]. Journal of Guidance, Control, and Dynamics, 2008, 31(4):1067-1075.
[8] XUE S B, LU P. Constrained predictor-corrector entry guidance[J]. Journal of Guidance, Control, and Dynamics, 2010, 33(4):1273-1281.
[9] LU P. Entry guidance:a unified method[J]. Journal of Guidance, Control, and Dynamics, 2014, 37(3):713-728.
[10] LU P, BRUNNER C W, STACHOWIAK S J, et al. Verification of a fully numerical entry guidance algorithm[J]. Journal of Guidance, Control, and Dynamics, 2016, 40(2):230-247.
[11] LU P, CERIMELE C J, TIGGES M A, et al. Optimal aerocapture guidance[J]. Journal of Guidance, Control, and Dynamics, 2015, 38(4):553-565.
[12] 李毛毛, 胡军. 火星进入段自适应预测校正制导方法[J]. 宇航学报, 2017, 38(5):506-515. LI M M, HU J. An adaptive predictor-corrector method of Mars entry phase[J]. Journal of Astronautics, 2017, 38(5):506-515(in Chinese).
[13] 胡军, 张钊. 载人登月飞行器高速返回再入制导技术研究[J]. 控制理论与应用, 2014, 31(12):1678-1685. HU J, ZHANG Z. A study on the reentry guidance for a manned lunar return vehicle[J]. Control Theory & Applications, 2014, 31(12):1678-1685(in Chinese).
[14] YU W B, CHEN W C, JIANG Z G, et al. Omnidirectional autonomous entry guidance based on 3-D analytical glide formulas[J]. ISA Transactions, 2016, 65:487-503.
[15] 胡锦川, 陈万春. 高超声速飞行器平稳滑翔弹道设计方法[J]. 北京航空航天大学学报, 2015, 41(8):1464-1475. HU J C, CHEN W C. Steady glide trajectory planning method for hypersonic reentry vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(8):1464-1475(in Chinese).
[16] YU W B, CHEN W C. Guidance law with circular no-fly zone constraint[J]. Nonlinear Dynamics, 2014, 78(3):1953-1971.
[17] 方科, 张庆振, 倪昆, 等. 高超声速飞行器时间协同再入制导[J]. 航空学报, 2018, 39(5):321958. FANG K, ZHANG Q Z, NI K, et al. Time-coordinated reentry guidance law for hypersonic vehicle[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(5):321958(in Chinese).
[18] SHEN Z J, LU P. Onboard generation of three-dimensional constrained entry trajectories[J]. Journal of Guidance, Control, and Dynamics, 2003, 26(1):111-121.
[19] 赵頔, 沈作军. 基于在线轨迹迭代的自适应再入制导[J]. 北京航空航天大学学报, 2016, 42(7):1526-1535. ZHAO D, SHEN Z J. Adaptive reentry guidance based on on-board trajectory iterations[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(7):1526-1535(in Chinese).
[20] 王肖, 郭杰, 唐胜景, 等. 基于解析剖面的时间协同再入制导[J]. 航空学报, 2019, 40(3):322565. WANG X, GUO J, TANG S J, et al. Time-cooperative entry guidance based on analytical profile[J]. Acta Aeronautica et Astronautica Sinica, 2019,40(3):322565(in Chinese).
[21] KARLGAARD C D, KUTTY P, SCHOENENBERGER M. Coupled inertial navigation and flush air data sensing algorithm for atmosphere estimation[J]. Journal of Spacecraft and Rockets, 2016, 54(1):128-140.
[22] 邵干, 张曙光, 唐鹏. 小型无人机气动参数辨识的新型HGAPSO算法[J]. 航空学报, 2017, 38(4):120365. SHAO G, ZHANG S G, TANG P.HGAPSO:A new aerodynamic parameters identification algorithm for small unmanned aerial vehicles[J]. Acta Aeronautica et Astronautica Sinica, 2017, 38(4):120365(in Chinese).
[23] 陈尔康, 荆武兴, 高长生. 弹性高超声速滑翔飞行器的状态/参数联合估计[J]. 航空学报, 2019, 40(8):322992. CHEN E K, JING W X, GAO C S. State/parameter joint estimation for flexible hypersonic glide vehicles[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40(8):322992(in Chinese).
[24] JOHN H M, KURTIS K F. Numerical methods using matlab[M]. New York:Person Education,Inc,2004:324-334.

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