Deep Learning-based Reentry Predictor-corrector Fault-tolerant Guidance for Hypersonic Vehicles

doi:10.3969/j.issn.1000-1093.2020.04.005

Abstract

Abstract: A deep learning-based predictor-corrector fault-tolerant guidance method is proposed for fault-tolerant guidance of hypersonic vehicles. In the design of longitudinal guidance law, the trimmable angle of attack profile and the coefficients of lift and drag in case of fault are calculated. A deep neural network which inputs contain variations of lift and drag coefficients is developed to predict landing point, thus avoiding large quantities of integral operations in traditional predictor-corrector guidance method. A bank angle reversal logic based on heading angle error corridor is designed for the lateral guidance. Extended state observer is constructed to estimate the variations of lift and drag coefficients, and the estimates are input into the deep neural network. Simulated result shows that the proposed fault-tolerant guidance algorithm has high guidance precision and excellent real-time characteristics and can calculate guidance command which meets flight requirements in real time. Key

Key words: hypersonicvehicle, fault-tolerantguidance, predictor-correctorguidance, deeplearning, extendedstateobserver

CLC Number:

V448.235

YU Yue， WANG Honglun. Deep Learning-based Reentry Predictor-corrector Fault-tolerant Guidance for Hypersonic Vehicles[J]. Acta Armamentarii, 2020, 41(4): 656-669.

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第41卷第4期2020 年4月
兵工学报ACTA ARMAMENTARII
Vol.41No.4Apr.2020

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