基于模糊差异游戏的三方无人机对抗系统的神经网络最佳控制
1School of Automation, Beijing Information Science and Technology University, Beijing, 100192, China. fxj@bistu.edu.cn.
Scientific reports
|September 15, 2024
概括
使用模糊差分游戏理论开发了三方无人机 (UAV) 系统的新型神经网络控制战略. 这种方法在复杂的空中对抗中优化了攻击者,防御者和目标的控制.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 游戏理论 游戏理论
背景情况:
- 无人驾驶飞行器 (UAV) 系统越来越复杂,涉及具有竞争目标的多个自主代理.
- 传统的控制策略与多代理无人机对抗的动态和对抗性质作斗争.
- 微分游戏理论和模糊逻辑为优化这些系统中的决策提供了潜在的框架.
研究的目的:
- 为三方无人机对抗系统 (攻击者,防御者,目标) 提出一种新的最佳控制策略.
- 为解决三方差分游戏模型直接解决方案的复杂性.
- 确保拟议的控制战略的稳定性和趋同性.
主要方法:
- 构建一个三方无人机相互对抗模型和一个非线性差异控制系统.
- 模糊评估和差异性游戏理论的应用,将游戏分解为攻击者-防御者和攻击者-目标子游戏.
- 使用具有自适应动态编程的评估神经网络来近似最佳值函数.
主要成果:
- 为攻击者,防御者和目标分开最佳控制策略的推导.
- 通过神经网络成功接近最佳值函数.
- 通过利亚普诺夫理论证明神经网络重量和控制系统稳定性的趋同.
结论:
- 拟议的基于模糊差异游戏的神经网络最佳控制策略在三方无人机对抗中是有效的.
- 该方法为复杂的多代理对抗场景中的分散控制提供了强大的框架.
- 模拟结果验证了设计的控制策略的有效性和稳定性.
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