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基于模糊微分博弈的三方无人机对抗系统神经网络最优控制

Neural network optimal control for tripartite UAV confrontation systems based on fuzzy differential game.

作者信息

Fu Xingjian, Yan Hang

机构信息

School of Automation, Beijing Information Science and Technology University, Beijing, 100192, China.

出版信息

Sci Rep. 2024 Sep 16;14(1):21547. doi: 10.1038/s41598-024-71844-y.

DOI:10.1038/s41598-024-71844-y
PMID:39278944
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11403010/
Abstract

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation.

摘要

针对由攻击者、防御者和目标组成的三方无人机对抗系统，提出了基于模糊微分博弈的神经网络最优控制策略。首先，构建三方无人机相互对抗模型并建立非线性微分控制系统。其次，结合模糊评价方法和微分博弈理论，将三方无人机分为攻击者 - 防御者和攻击者 - 目标两个对抗博弈部分，分别推导攻击者、防御者和目标方的最优控制策略。然后，考虑到三方无人机博弈模型直接求解困难，引入评价神经网络，采用自适应动态规划方法逼近最优值函数。利用李雅普诺夫稳定性理论证明了评价神经网络权重的收敛性和非线性微分控制系统的稳定性。最后，通过仿真验证了本文设计的三方无人机对抗博弈控制策略的有效性。

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