Networked Control Systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. In this paper we review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies.
Summary. This chapter addresses the control of spatially distributed processes via communication networks with a fixed delay. A distributed architecture is utilized in which multiple local controllers coordinate their efforts through a data network that allows information exchange. We focus our work on linear time invariant processes disturbed by Gaussian white noise and propose several logics to determine when the local controllers should communicate. Necessary conditions are given under which these logics guarantee boundedness and the trade-off is investigated between the amount of information exchanged and the performance achieved. The theoretical results are validated through Monte Carlo simulations. The resulting closed-loop systems evolve according to stochastic differential equations with resets triggered by stochastic counters. This type of stochastic hybrid system is interesting on its own.
Abstract-This paper addresses the control of spatially distributed processes over a network that imposes bandwidth constraints and communication delays. Optimal communication policies are derived for an estimator-based Networked Control System architecture to reduce the communication load. These policies arise as solutions of an average cost optimization problem, which is solved using dynamic programming. The optimal policies are shown to be deterministic.
Abstract-An LTI estimation framework is proposed for networked control systems (NCS), in which local Kalman filter estimates are sent to the remote estimator. Both controlled and uncontrolled data communications are considered. For uncontrolled communication, minimum rate requirements are given for stochastic moment stability, which depend only on the least stable poles. For controlled communication, sufficient stability conditions are formulated. The framework also makes it possible to improve the trade-off between estimation performance and communication cost.
This paper addresses the control of spatially distributed processes. We utilize a distributed architecture in which multiple local controllers coordinate their efforts through a data network that allows information exchange. We focus our work on linear time invariant processes disturbed by Gaussian white noise and propose several logics to determine when the local controllers should communicate. We provide conditions under which these logics guarantee boundedness and investigate the trade-off between the amount of information exchanged and the performance achieved. The resulting closed-loop systems evolve according to stochastic differential equations with resets triggered by stochastic counters. This type of stochastic hybrid system seems to be interesting on its own. The theoretical results are validated through Monte Carlo simulations.
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