Optimal sensor scheduling under intermittent observations subject to network dynamics

Motivated by various distributed control applications, we consider a linear system with Gaussian noise observed by multiple sensors which transmit measurements over a dynamic lossy network. We characterize the stationary optimal sensor scheduling policy for the finite horizon, discounted, and long-t...

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Bibliographic Details
Main Authors Hmedi, Hassan, Carroll, Johnson, Arapostathis, Ari
Format Journal Article
LanguageEnglish
Published 15.12.2019
Subjects
Computer Science - Systems and Control
Mathematics - Optimization and Control
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DOI10.48550/arxiv.1912.07107

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Summary:Motivated by various distributed control applications, we consider a linear system with Gaussian noise observed by multiple sensors which transmit measurements over a dynamic lossy network. We characterize the stationary optimal sensor scheduling policy for the finite horizon, discounted, and long-term average cost problems and show that the value iteration algorithm converges to a solution of the average cost problem. We further show that the suboptimal policies provided by the rolling horizon truncation of the value iteration also guarantee stability and provide near-optimal average cost. Lastly, we provide qualitative characterizations of the multidimensional set of measurement loss rates for which the system is stabilizable for a static network, significantly extending earlier results on intermittent observations.
DOI:10.48550/arxiv.1912.07107