对于异步非线性系统的递归非统一采样估计器
Yu-Hang Yang1, Jin-Gang Liu1, Shen-Min Song1
1Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
本研究解决了带有数据包损失的非线性系统中的异步估计挑战. 它引入了一种新的观察推断方法,用于准确的状态估计,即使是不完美的系统模型.
科学领域:
- 控制系统工程 控制系统工程
- 非线性系统分析 非线性系统分析
- 信号处理 信号处理
背景情况:
- 异步采样和数据包损失使非线性系统中的状态估计变得复杂.
- 由于固有的不确定性和未建模的动态,通常很难获得准确的动态模型.
- 现有的估计方法可能难以应对异步数据和数据丢失的综合挑战.
研究的目的:
- 开发一种可靠的状态估计方法,用于随机采样经历数据包丢失的非线性系统.
- 通过加权状态更新来解决异步系统中的同步问题.
- 提出一种观测推理技术,尽管存在建模错误,但可以提高估计准确度.
主要方法:
- 通过相邻更新点的状态加权来同步异步采样.
- 在采样实例中应用投影定理用于状态估计.
- 使用插值技术开发观察推断,以处理建模不确定性.
- 在多传感器系统中扩展分布式聚变估计算法.
主要成果:
- 异步采样系统的成功同步.
- 有效的状态估计,即使在控制和测量点有数据包损失.
- 通过观察推断提高估计准确性,减轻模拟错误带来的挑战.
- 针对多传感器应用的分布式聚变估计器的验证.
结论:
- 提出的观察推断方法有效地处理带有数据包损失的非线性系统中的异步估计.
- 开发的算法为在具有挑战性的系统动态中进行状态估计提供了强大的解决方案.
- 扩展到多传感器系统表明了算法的多功能性和实际应用的潜力.
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