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

2022/05/30 by Chi Peng, Hanwen Zhang, Yongxiang He +1 · 1 citation
Engineering · Computer Science · #Guidance and Control Systems #Adaptive Dynamic Programming Control #Adaptive Control of Nonlinear Systems

paper · doi:10.1109/taes.2022.3178770

Abstract

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.

Citations

• Online actor–critic algorithm to solve the continuous-time infinite horizon optimal control problem

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• Learning-Based Multi-Stage Strategy for a Fixed-Wing Aircraft to Evade a Missile Detected at a Short Distance

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