论文标题
基于事件的控制随机线性系统与分布式估计的应用
Event-Based Control for Synchronization of Stochastic Linear Systems with Application to Distributed Estimation
论文作者
论文摘要
本文研究了随机线性系统的同步，该系统受到一般噪声的同步，从某种意义上说，噪声是在协方差界定的，但可能与试剂状态以及彼此之间的相关性。我们提出了一种基于事件的控制协议，以通过使用随机的Lyapunov功能来实现均等意义上的代理之间的同步，并理论上分析了它的性能，在这种情况下，$ c $ -martingales的稳定性尤其是为了处理由NOISES的一般模型带来的挑战和事件刺激机制带来的挑战。然后，应用了基于事件的同步算法来解决传感器网络中分布式估计的问题。具体而言，通过无损分解最佳的卡尔曼过滤器，可以通过使用设计用于实现随机线性系统同步的算法来解决分布式估计的问题。因此，开发了基于事件的分布式估计算法，每个传感器仅使用其自己的测量来执行局部过滤，以及所提出的基于事件的同步算法，以融合相邻节点的局部估计。随着通信频率的降低，在网络连接性和集体系统可观察性的最低要求下，设计的估计器被证明是稳定的。
This paper studies the synchronization of stochastic linear systems which are subject to a general class of noises, in the sense that the noises are bounded in covariance but might be correlated with the states of agents and among each other. We propose an event-based control protocol for achieving the synchronization among agents in the mean square sense and theoretically analyze the performance of it by using a stochastic Lyapunov function, where the stability of $c$-martingales is particularly developed to handle the challenges brought by the general model of noises and the event-triggering mechanism. The proposed event-based synchronization algorithm is then applied to solve the problem of distributed estimation in sensor network. Specifically, by losslessly decomposing the optimal Kalman filter, it is shown that the problem of distributed estimation can be resolved by using the algorithms designed for achieving the synchronization of stochastic linear systems. As such, an event-based distributed estimation algorithm is developed, where each sensor performs local filtering solely using its own measurement, together with the proposed event-based synchronization algorithm to fuse the local estimates of neighboring nodes. With the reduced communication frequency, the designed estimator is proved to be stable under the minimal requirements of network connectivity and collective system observability.
