In this article, a state-following-kernel-based reinforcement learning method with an extended disturbance observer is proposed, whose application to a missile-target interception system is considered. First, the missile-target engagement is formulated as a vertical planar pursuit–evasion problem. The target maneuver is then estimated by an extended disturbance observer in real time, which leads to an infinite-horizon optimal regulation problem. Next, utilizing the local state approximation ability of state-following kernels, the critic neural network (NN) and actor NN for synchronous iteration are constructed to calculate the approximate optimal guidance policy. The states and NN weights are proven to be uniformly ultimately bounded using the Lyapunov method. Finally, numerical simulations against different types of nonstationary targets are effectively tested, and the results highlight the role of state-following kernels in the value function and policy approximation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    State-Following-Kernel-Based Online Reinforcement Learning Guidance Law Against Maneuvering Target


    Contributors:
    Peng, Chi (author) / Zhang, Hanwen (author) / He, Yongxiang (author) / Ma, Jianjun (author)


    Publication date :

    2022-12-01


    Size :

    5291164 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Information set-based guidance algorithm against a decelerating maneuvering target

    Emeliyanov, D. / Rubinovich, E. / Miller, B. | IEEE | 2005


    A New Missile Guidance Algorithm Against a Maneuvering Target

    Imado, F. / Kuroda, T. / Tahk, M.-J. et al. | British Library Conference Proceedings | 1998



    Fixed-Time Convergence Cooperative Guidance Law Against Maneuvering Target

    Zhang, Mingjie / Ma, Jianjun / Che, Rong et al. | TIBKAT | 2022