Advertisement

PSO Rapid Ascending Trajectory Planning Method Based on Neural Network Trajectory Surrogate Model

  • Yuhang Zeng
  • Ye Yang
  • Yongji Wang
  • Lei LiuEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)

Abstract

To reduce the time cost of Runge-Kutta multi-step prediction calculation process of PSO direct shooting method in solving trajectory planning problem, a fast trajectory planning method based on neural network surrogate model converting multi-step prediction process into single-step prediction is proposed. In this method, the solution space samples consisting of feasible trajectories are designed to approximate the real feasible trajectory solution space, and the training set of neural network is constructed by feasible trajectory database. The samples generation time is reduced by trajectory reusing, and the incremental method of distinguishing reference state vectors at different times is used to reduce the complexity of the model so as to facilitate the training of the model of the neural network. The simulation results show that the method is fast, feasible and adaptable.

Keywords

Surrogate model Neural network Particle swarm optimization Ascending trajectory planning Shooting method 

Notes

Acknowledgements

This work was supported in part by the National Nature Science Foundation of China (Grant nos. 61873319, 61803162 and 61573161).

References

  1. 1.
    Betts, J.T.: Survey of numerical methods for trajectory optimization. J. Guid. Control Dyn. 21(2), 193–207 (1998)CrossRefGoogle Scholar
  2. 2.
    Changwan, M., Jianping, Y.: Introduction of military aircraft route planning. Flight Dyn. 4 (1998)Google Scholar
  3. 3.
    Yakimenko, O.A.: Direct method for rapid prototyping of near-optimal aircraft trajectories. J. Guid. Control Dyn. 23(5), 865–875 (2000)CrossRefGoogle Scholar
  4. 4.
    Huang, G.Q., Lu, Y.P., Nan, Y.: A survey of numerical algorithms for trajectory optimization of flight vehicles. Sci. China Technol. Sci. 55(9), 2538–2560 (2012)CrossRefGoogle Scholar
  5. 5.
    Verma, A., et al.: Neural dynamic trajectory design for reentry vehicles. In: AIAA Guidance, Navigation and Control Conference and Exhibition (2007)Google Scholar
  6. 6.
    Julian, K.D., Kochenderfer, M.J.: Neural network guidance for UAVs. In: AIAA Guidance, Navigation, and Control Conference (2017)Google Scholar
  7. 7.
    Zhang, B., Chen, S., Xu, M.: Application of neural network in trajectory planning of the entry vehicle for variable targets. In: International Conference on Artificial Intelligence and Computational Intelligence. Springer, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Dileep, M.V., Kamath, S., Nair, V.G.: Ascent phase trajectory optimization of launch vehicle using theta-particle swarm optimization with different thrust scenarios. Int. Rev. Aerospace Eng. 9(6), 200–207 (2016)CrossRefGoogle Scholar
  9. 9.
    Breitner, M.H.: Robust optimal onboard reentry guidance of a space shuttle: dynamic game approach and guidance synthesis via neural networks. J. Optim. Theory Appl. 107(3), 481–503 (2000)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Rao, A.V.: A survey of numerical methods for optimal control. Adv. Astronaut. Sci. 135(1), 497–528 (2009)Google Scholar
  11. 11.
    Huang, J., Qian, J., Liu, L., Wang, Y.J., Xiong, C.H., Ri, S.: Echo state network based predictive control with particle swarm optimization for pneumatic muscle actuator. J. Franklin Inst. 353, 2761–2782 (2016)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hou, Z.W., Liu, L., Wang, Y.J., Huang, J., Fan, H.J.: Terminal impact angle constraint guidance with dual sliding surfaces and model-free target acceleration estimator. IEEE Trans. Control Syst. Technol. 25(1), 85–100 (2017)CrossRefGoogle Scholar
  13. 13.
    Liu, X., Liu, L., Wang, Y.J.: Minimum time state consensus for cooperative attack of multi-missile systems. Aerosp. Sci. Technol. 69, 87–96 (2017)CrossRefGoogle Scholar
  14. 14.
    Geiger, B., Horn, J.: Neural network based trajectory optimization for unmanned aerial vehicles. In: 47th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition (2009)Google Scholar
  15. 15.
    Xue, M.: UAV trajectory modeling using neural networks. In: 17th AIAA Aviation Technology, Integration, and Operations Conference (2017)Google Scholar
  16. 16.
    Murillo, O., Lu, P.: Fast ascent trajectory optimization for hypersonic air-breathing vehicles. In: AIAA Guidance, Navigation, and Control Conference (2010)Google Scholar
  17. 17.
    Sagliano, M., Mooij, E., Theil, S.: Onboard trajectory generation for entry vehicles via adaptive multivariate pseudospectral interpolation. In: AIAA Guidance, Navigation, and Control Conference (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National Key Laboratory of Science and Technology on Multispectral Information Processing, School of Artificial Intelligence and AutomationHUSTWuhanChina
  2. 2.Beijing Aerospace Automatic Control Institute, Science and Technology on Aerospace Intelligent Control LaboratoryBeijingChina

Personalised recommendations