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

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.