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A deep learning-based approach to time-coordination entry guidance for multiple hypersonic vehicles

Published online by Cambridge University Press:  10 February 2023

Z. Li ,
J. Guo ,
S. Tang Open the ORCID record for S. Tang [Opens in a new window]  and
S. Ji Open the ORCID record for S. Ji [Opens in a new window]
Z. Li
Affiliation:
Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100,081, China
J. Guo*
Affiliation:
Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100,081, China
S. Tang
Affiliation:
Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100,081, China
S. Ji
Affiliation:
Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education, School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100,081, China
*
*Correspondence author. Email: guojie1981@bit.edu.cn
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Abstract

A multiple-vehicles time-coordination guidance technique based on deep learning is suggested to address the cooperative guiding problem of hypersonic gliding vehicle entry phase. A dual-parameter bank angle profile is used in longitudinal guiding to meet the requirements of time coordination. A vehicle trajectory database is constructed along with a deep neural network (DNN) structure devised to fulfill the error criteria, and a trained network is used to replace the conventional prediction approach. Moreover, an extended Kalman filter is constructed to detect changes in aerodynamic parameters in real time, and the aerodynamic parameters are fed into a DNN. The lateral guiding employs a logic for reversing the sign of bank angle, which is based on the segmented heading angle error corridor. The final simulation results demonstrate that the built DNN is capable of addressing the cooperative guiding requirements. The algorithm is highly accurate in terms of guiding, has a fast response time, and does not need inter-munition communication, and it is capable of solving guidance orders that satisfy flight requirements even when aerodynamic parameter disruptions occur.

Keywords

Hypersonic glide vehiclesCooperative entry guidanceNeural networkDeep learningExtended Kalman filter

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 127 , Issue 1310 , April 2023 , pp. 604 - 626
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Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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References

Li, G., Zhang, H., & Tang, G. Flight-corridor analysis for hypersonic glide vehicles, J Aerosp Eng, January 2017, 30, (1), p. 06016005.CrossRefGoogle Scholar
Phillips, T.H. A common aero vehicle (CAV) model, description, and employment guide, Schafer Corporation for AFRL and AFSPC, 2003, 27.Google Scholar
Walker, S., Sherk, J., Shell, D., Schena, R., Bergman, J., & Gladbach, J. The DARPA/AF falcon program: The hypersonic technology vehicle# 2 (HTV-2) flight demonstration phase, 15th AIAA International Space Planes and Hypersonic Systems and Technologies Conference, 2008.CrossRefGoogle Scholar
Gao, Y., Zhang, M., & Jia, C. Development review of world air and missile defense system in 2016, Tactical Missile Technol, February 2017, (2), pp 16–20.Google Scholar
Liang, Z.X. Research on three-dimensional guidance for maneuverable hypersonic gliding vehicles, Beihang University, Beijing, 2016.Google Scholar
Shen, Z., & Lu, P. Onboard generation of three-dimensional constrained entry trajectories, J Guid Cont Dynam, May 2012, 2003, 26, pp 111–121.CrossRefGoogle Scholar
Zhang, Y.L., Chen, K.J., Liu, L.H., Tang, G.J., & Bao, W.M. Entry trajectory planning based on three-dimensional acceleration profile guidance, Aerosp Sci Technol, January 2016, 2016, 48, pp 131–139.CrossRefGoogle Scholar
Joshi, A., Sivan, K., & Amma, S.S. Predictor-corrector reentry guidance algorithm with path constraints for atmospheric entry vehicles, J Guid Cont Dynam, September 2007, 30, (5), pp 1307–1318.CrossRefGoogle Scholar
He, S., Wang, W., Lin, D. and Lei, H. Consensus-Based Two-Stage Salvo Attack Guidance, IEEE Trans Aerosp Electron Syst, June 2018, 54, (3), pp 1555–1566.CrossRefGoogle Scholar
Beard, R.W., McLaint, T.W., & Goodricht, M. Coordinated target assignment and intercept for unmanned air vehicles, IEEE Trans Robot Automat, December 2002, 18, (6), pp 911–922.CrossRefGoogle Scholar
Campbell, M.E., & Whitacre, W.W. Cooperative tracking using vision measurements on SeaScan UAVs, IEEE Trans Contr Syst Technol, July 2007, 15, (4), pp 613–626.CrossRefGoogle Scholar
Zong, Q., Wang, D., Shao, S., Zhang, B. and Yu, H. Research status and development of multi UAV coordinated formation flight control, J Harbin Inst Technol, March 2017, 49, (3), pp 1–14.Google Scholar
Zhao, S.Y., Zhou, R. Muti-missile cooperative guidance using coordination variables. Acta Aeronaut Astronaut Sin, November 2008, 29, (6), pp 1605–1611.Google Scholar
Zhang, Y.A., Ma, G.X., & Wang, X.P. Time-cooperative guidance for multi-missiles: A leader-follower strategy. Acta Aeronaut Astronaut Sin, June 2009, 30, (6), pp 1109–1118.Google Scholar
Kumar, S.R., & Ghose, D. Cooperative rendezvous guidance using sliding mode control for interception of stationary targets, IFAC Proc Vol, 2014, 47, (1), pp 477–483.CrossRefGoogle Scholar
Liang, Z.X., Yu, J., Ren, Z., & Li, Q. Trajectory planning for cooperative flight of two hypersonic entry vehicles, 21st AIAA International Space Planes and Hypersonics Technologies Conference, American Institute of Aeronautics and Astronautics, Xiamen, China, 2017.CrossRefGoogle Scholar
Yu, J.L., Dong, X.W., Li, Q.D., Ren, Z., & Lv, J.H. Cooperative guidance strategy for multiple hypersonic gliding vehicles system, Chin J Aeronaut, March 2020, 33, (3), pp 990–1005.CrossRefGoogle Scholar
Fang, K., Zhang, Q.Z., Kun, N.I., Cheng, L., & Huang, Y.T. Time-coordination reentry guidance law for hypersonic vehicle, Acta Aeronaut Astronaut Sinica, May 2018, 39, (5), pp 202–217.Google Scholar
Zhang, W.Q., Yu, W.B., Li, J.L. and Chen, W.C. Cooperative reentry guidance for intelligent lateral maneuver of hypersonic vehicle based on downrange analytical solution, Acta Armamentarii, July 2021, 42, (7), pp 1400–1411.Google Scholar
Wang, X., Guo, J., Tang, S.J., & Qi, S. Time-cooperative entry guidance based on analytical profile, Acta Aeronaut Astronaut Sin, March 2019, 40, (3), pp 239–250.Google Scholar
Li, Z.H., He, B., Wang, M.H., Lin, H.S., & An, X.B. Time-coordination entry guidance for multi-hypersonic vehicles, Aerosp Sci Technol, June 2019, 89, pp 123–135.CrossRefGoogle Scholar
Mease, K.D., Chen, D.T., Teufel, P., & Schonenberger, H. Reduced-order entry trajectory planning for acceleration guidance, J Guid Cont Dynam, March 2002, 25, (2), pp 257–266.CrossRefGoogle Scholar
Jorris, T.R., & Cobb, R.G. Three-dimensional trajectory optimization satisfying waypoint and no-fly zone constraints, J Guid Cont Dynam, March 2009, 32, (2), pp 551–572.CrossRefGoogle Scholar
Lu, P. Entry guidance: A unified method, J Guid Cont Dynam, April 2014, 37, (3), pp 713–728.CrossRefGoogle Scholar
Li, Z.H., Hu, C., Ding, C.B., Liu, G., & He, B. Stochastic gradient particle swarm optimization based entry trajectory rapid planning for hypersonic glide vehicles, Aerosp Sci Technol, May 2018, 76, pp 176–186.CrossRefGoogle Scholar
Wang, X., Guo, J., Tang, S.J., Qi, S., & Wang, Z.Y. Entry trajectory planning with terminal full states constraints and multiple geographic constraints, Aerosp Sci Technol, January 2019, 84, pp 620–631.CrossRefGoogle Scholar
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. Learning representations by back-propagating errors, Nature, October 1986, 323, (6088), pp 533–536.CrossRefGoogle Scholar
Li, J., & Zhang, M. On deep-learning-based geometric filtering in aerodynamic shape optimization, Aerosp Sci Technol, May 2021, 112, p 106603.CrossRefGoogle Scholar
Liu, Y., Wang, H., Fan, J., Wu, J., & Wu, T. Control-oriented UAV highly feasible trajectory planning: A deep learning method, Aerosp Sci Technol, March 2021, 110, p 106435.CrossRefGoogle Scholar
Shen, Z., & Lu, P. Dynamic lateral entry guidance logic, J Guid Cont Dynam, November 2004, 27, (6), pp 949–959.CrossRefGoogle Scholar
Evensen, G. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J Geophys Res, May 1994, 99, (C5), pp 10143–10162.CrossRefGoogle Scholar
Phillips, T.H. A common aero vehicle (CAV) model, description, and employment guide, Arlington, VA, US: Schafer Corporation for AFRL and AFSPC, 2003. Google Scholar