适应性神经滑动模式控制用于模糊的奇异扰乱系统:基于停留的概率的随机通信协议
IEEE transactions on cybernetics
|July 30, 2024
概括
本研究介绍了适应性神经网络的滑动模式控制策略,用于面临欺骗攻击和随机协议的模糊系统. 这种新的方法确保了系统的稳定性和性能,尽管存在不确定性和对抗性行为.
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
- 系统动力学系统动力学
背景情况:
- 模糊的单一扰乱系统容易受到欺骗攻击和随机通信协议 (SCP) 的影响.
- 使用过渡概率的传统SCP表征可能无法完全捕捉系统的随机性.
- 模糊控制器中不完美的前提匹配可能会降低性能.
研究的目的:
- 开发适应性神经网络 (NN) 滑动模式控制 (SMC) 策略,用于模糊的单一扰乱系统.
- 为了增强对不受限制的欺骗攻击和随机通信协议的稳定性.
- 为了改善随机行为的特征,使用基于逗留概率的SCP.
主要方法:
- 提出了一个适应性的NN-SMC策略,整合模糊规则和奇异扰动参数.
- 一个基于逗留概率的SCP是为了准确的随机表征而建立的.
- 基于NN的技术用于估计和减轻欺骗攻击的影响.
主要成果:
- 控制器的设计解决了不完美的前提匹配挑战.
- 对于闭环系统,平均平方意义上的指数极限性是保证的.
- 确保指定滑动表面的可达性.
结论:
- 拟议的适应性NN-SMC框架为模糊的单一扰乱系统提供了强大的解决方案.
- 该策略有效地处理欺骗攻击和复杂的随机通信协议.
- 通过示例验证证实了控制方法的实际适用性和弹性.
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