Learning prediction-correction guidance for impact time control

Date

2021-10-28

Supervisor/s

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Department

Type

Article

ISSN

1270-9638

Format

Free to read from

Citation

Liu Z, Wang J, He S, et al., (2021) Learning prediction-correction guidance for impact time control. Aerospace Science and Technology, Volume 119, December 2021, Article number 107187

Abstract

This paper investigates the problem of impact-time-control and proposes a learning-based computational guidance algorithm to solve this problem. The proposed guidance algorithm is developed based on a general prediction-correction concept: the exact time-to-go under proportional navigation guidance with realistic aerodynamic characteristics is estimated by a deep neural network and a biased command to nullify the impact time error is developed by utilizing the emerging reinforcement learning techniques. To deal with the problem of insufficient training data, a transfer-ensemble learning approach is proposed to train the deep neural network. The deep neural network is augmented into the reinforcement learning block to resolve the issue of sparse reward that has been observed in typical reinforcement learning formulation. Extensive numerical simulations are conducted to support the proposed algorithm.

Description

Software Description

Software Language

Github

Keywords

Missile guidance, Impact-time-control guidance, Prediction-correction, Transfer learning, Reinforcement learning

DOI

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

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