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Cited by 355

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Trading computation for bandwidth: reducing communication in distributed control systems using state estimators

Yook, J.K., Tilbury, D.M., Soparkar, N.R.·2002·IEEE Transactions on Control Systems Technology

Abstract

Describes a new framework for distributed control systems in which estimators are used at each node to estimate the values of the outputs at the other nodes. The estimated values are then used to compute the control algorithms at each node. When the estimated value deviates from the true value by more than a pre-specified tolerance, the actual value is broadcast to the rest of the system; all of the estimators are then updated to the current value. By using the estimated values instead of true value at every node, a significant saving in the required bandwidth is achieved, allowing large-scale distributed control systems to be implemented effectively. The stability, performance, and expected communication frequency of the reduced communication system are analyzed in detail. Simulation and experimental results validating the effectiveness and communication savings of the framework are also presented.

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Metadata

Authors
Yook, J. K., Tilbury, D. M., & Soparkar, N. R.
Venue
IEEE Transactions on Control Systems Technology
Published
2002-07
Volume
10
Issue
4
Pages
503-518
ISSN
1063-6536 (ISSN-L)
OpenAlex
W2103013786
Cited by
355
References
6
FWCI
25.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Trading computation for bandwidth: reducing communication in distributed control systems using state estimators. https://app.cochat.ai/papers/10.1109/tcst.2002.1014671
The paper
Yook, J. K., Tilbury, D. M., & Soparkar, N. R. (2002). Trading computation for bandwidth: reducing communication in distributed control systems using state estimators. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2002.1014671

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.