适应性神经共识观察员网络设计,用于一类半线性抛物线PDE系统
IEEE transactions on neural networks and learning systems
|April 10, 2024
概括
本研究介绍了适应观察器,用于估计抛物线偏微分方程 (PDE) 系统中的状态和不确定性. 新的共识方法确保了准确的估计,即使在参数和非参数不确定性.
科学领域:
- 控制系统工程 控制系统工程
- 部分微分方程 部分微分方程
- 适应式观察者设计
背景情况:
- 准确的状态和不确定性估计对于复杂系统至关重要.
- 抛物线部分微分方程 (PDE) 系统由于其分布性和不确定性而存在独特的挑战.
- 现有的方法经常与同时估计状态以及参数和非参数不确定性作斗争.
研究的目的:
- 开发一种强大的基于共识的方法,用于对抛物线PDE系统的联合状态不确定性估计.
- 使用自适应观察者网络来解决参数和非参数不确定性.
- 为有效的不确定性识别设计新的适应法.
主要方法:
- 建议建立一个由知情和不知情的边界观察员组成的双层网络.
- 制定了新的适应法,其中包含了观察者间参数估计不匹配处罚.
- 辐射基函数 (RBF) 神经网络用于近似非参数不确定性.
主要成果:
- 拟议的自适应观察员网络实现了对参数不确定性的指数联合状态不确定性估计.
- 在持久激发条件下,对于非参数不确定性实现了终极边界估计.
- 利亚普诺夫稳定理论验证了估计性能的理论保证.
结论:
- 开发的共识方法有效地提高了对抛物线PDE系统的联合状态不确定性估计.
- 该方法在存在各种不确定性类型的情况下表现出强的性能.
- 对反应扩散系统的数值模拟证实了拟议方法的有效性和令人信服的发现.
相关概念视频
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01:26Classification of Systems-I
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