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.
State-Following-Kernel-Based Online Reinforcement Learning Guidance Law Against Maneuvering Target
IEEE Transactions on Aerospace and Electronic Systems ; 58 , 6 ; 5784-5797
2022-12-01
5291164 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Deep reinforcement learning guidance law for intercepting random maneuvering target spacecraft
Europäisches Patentamt | 2024
|Intercept Guidance of Maneuvering Targets with Deep Reinforcement Learning
DOAJ | 2023
|Information Set-Based Guidance Algorithm against a Decelerating Maneuvering Target
Online Contents | 2005
|Fixed-Time Convergence Cooperative Guidance Law Against Maneuvering Target
Springer Verlag | 2021
|