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

  • Yuhang Zeng
  • Ye Yang
  • Yongji Wang
  • Lei LiuEmail author
Conference paper
First Online:
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 
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Notes

Acknowledgements

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

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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

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