论文标题
共同连接的开关网络上线性系统的分布式指数状态估计
Distributed exponential state estimation of linear systems over jointly connected switching networks
论文作者
论文摘要
最近，在共同连接的开关网络上解决连续时间线性系统的分布式状态估计问题。结果表明，估计误差将通过使用广义的Barbalat的引理渐近地收敛到原点。本文通过两个新功能进一步研究了同样的问题。首先，将渐近收敛加强到指数收敛。这种加强的结果不仅提供了保证的收敛速度，而且还使误差系统总稳定性使得能够承受小的干扰。其次，我们本地观察者的耦合收益可能是不同的，因此具有更大的设计灵活性，而现有结果中的耦合增益必须相同。这两个新功能是通过建立两类线性时间变化系统的指数稳定性来实现的，这些系统可能具有其他应用。
Recently, the distributed state estimation problem for continuous-time linear systems over jointly connected switching networks was solved. It was shown that the estimation errors will asymptotically converge to the origin by using the generalized Barbalat's Lemma. This paper further studies the same problem with two new features. First, the asymptotic convergence is strengthened to the exponential convergence. This strengthened result not only offers a guaranteed convergence rate, but also renders the error system total stability and thus is able to withstand small disturbances. Second, the coupling gains of our local observers can be distinct and thus offers greater design flexibility, while the coupling gains in the existing result were required to be identical. These two new features are achieved by establishing exponential stability for two classes of linear time-varying systems, which may have other applications.
