In this article, an intelligent predictor–corrector entry guidance approach for lifting hypersonic vehicles is proposed to achieve real-time and safe control of entry flights by leveraging the deep neural network (DNN) and constraint management techniques. First, the entry trajectory planning problem is formulated as a univariate root-finding problem based on a compound bank angle corridor, and two constraint management algorithms are presented to enforce the satisfaction of both path and terminal constraints. Second, a DNN is developed to learn the mapping relationship between the flight states and ranges, and experiments are conducted to verify its high approximation accuracy. Based on the DNN-based range predictor, an intelligent, multiconstrained predictor–corrector guidance algorithm is developed to achieve real-time trajectory correction and lateral heading control with a determined number of bank reversals. Simulations are conducted through comparing with the state-of-the-art predictor-corrector algorithms, and the results demonstrate that the proposed DNN-based entry guidance can achieve the trajectory correction with an update frequency of 20 Hz and is capable of providing high-precision, safe, and robust entry guidance for hypersonic vehicles.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multiconstrained Real-Time Entry Guidance Using Deep Neural Networks


    Contributors:
    Cheng, Lin (author) / Jiang, Fanghua (author) / Wang, Zhenbo (author) / Li, Junfeng (author)


    Publication date :

    2021-02-01


    Size :

    3041264 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    MULTI-CONSTRAINED REAL-TIME ENTRY GUIDANCE USING DEEP NEURAL NETWORKS

    Cheng, Lin / Wang, Zhenbo / Jiang, Fanghua et al. | TIBKAT | 2020



    Intelligent Online Multiconstrained Reentry Guidance Based on Hindsight Experience Replay

    Qingji Jiang / Xiaogang Wang / Yuliang Bai et al. | DOAJ | 2023

    Free access