In the majority of research on guidance laws, missiles are commonly simplified as point masses. This approach may have adverse effects on the practical application of research outcomes. Additionally, conventional neural network-trained reinforcement learning (RL) guidance laws suffer from acceleration command oscillations (ACO) when the missile approaches the target, which can detrimentally affect the guidance performance. In order to tackle these issues, this study introduces a three-degree-of-freedom model that incorporates the missile body and controller in the training environment. Aiming to simulate the flight dynamics of missiles as closely to reality as possible. Furthermore, a gated recurrent unit layer is introduced in the neural network to allow the missile to leverage previous information and reduce chattering in output commands. Finally, the performance of the designed guidance law is validated through multiple repeated simulation experiments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Reinforcement Learning-Based Intelligent Guidance Law for Air-to-Air Missiles


    Contributors:
    Gu, Yupeng (author) / Tang, Wei (author) / Du, Suang (author)


    Publication date :

    2023-06-16


    Size :

    2062443 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A Cooperative Guidance Law for Multiple Missiles based on Reinforcement Learning

    Chen, Hongxu / Yu, Jianglong / Dong, Xiwang | IEEE | 2022




    Guidance -- Brains of missiles

    Sigley, D.T. / Korman, N.I. / Worsley, G. | Engineering Index Backfile | 1956


    Celestial guidance for missiles

    Strong, J.G. | Engineering Index Backfile | 1952