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Distributed Dynamic State Estimation and LQG Control in Resource-Constrained Networks

机译:资源受限网络中的分布式动态状态估计和LQG控制

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In this paper, the discrete-time distributed dynamic state estimation and linear quadratic Gaussian (LQG) control problems are analyzed for resource-constrained networked systems. Following a holistic approach, we provide a complete system design for the signal processing, communication, and control tasks involved in the problems; and evaluate their performance. In the presence of a controller node and a number of sensor nodes, the sensor nodes, in a resource-efficient way, report their information entities to the controller node using an event-triggered sampling technique called level-crossing sampling. We demonstrate the performance gains due to level-crossing sampling over conventional time-triggered uniform sampling, as well as the advantages of processing data locally before transmitting to the controller. In particular, it is shown that the proposed decentralized schemes with local processing and level-crossing sampling ensure a very close approximation, with a bounded error, to the optimum (centralized) estimation and control schemes, and as a result yield order-2 asymptotic optimality. Moreover, nonideal communication between sensors and the controller is considered, and optimal modulation techniques are provided for different channel models. Simulation results are provided to support the presented discussions.
机译:本文分析了资源受限的网络系统的离散时间分布式动态状态估计和线性二次高斯(LQG)控制问题。按照整体方法,我们为涉及问题的信号处理,通信和控制任务提供了完整的系统设计;并评估他们的表现。在存在控制器节点和多个传感器节点的情况下,传感器节点以一种资源高效的方式,使用一种称为水平交叉采样的事件触发采样技术将其信息实体报告给控制器节点。我们证明了与传统的时间触发的均匀采样相比,跨电平采样带来的性能提升,以及在传输给控制器之前在本地处理数据的优势。特别地,表明所提出的具有局部处理和水平交叉采样的分散方案确保了对最优(集中)估计和控制方案的非常接近的近似,但有一定的误差,结果是产生了2阶渐近。最优性。而且,考虑了传感器和控制器之间的非理想通信,并且针对不同的信道模型提供了最佳的调制技术。提供仿真结果以支持提出的讨论。

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