Optimal sensor scheduling for multiple linear dynamical systems.
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| Title: | Optimal sensor scheduling for multiple linear dynamical systems. |
|---|---|
| Authors: | Han, Duo1,2 dhanaa@ntu.edu.sg, Wu, Junfeng3 junfengw@kth.se, Zhang, Huanshui4 hszhang@sdu.edu.cn, Shi, Ling2 eesling@ust.hk |
| Source: | Automatica. Jan2017, Vol. 75, p260-270. 11p. |
| Subjects: | Markov processes, Dynamical systems, Parameter estimation, Optimal control theory, Kalman filtering |
| Abstract: | We consider the design of an optimal collision-free sensor schedule for a number of sensors which monitor different linear dynamical systems correspondingly. At each time, only one of all the sensors can send its local estimate to the remote estimator. A preliminary work for the two-sensor scheduling case has been studied in the literature. The generalization into multiple-sensor scheduling case is shown to be nontrivial. We first find a necessary condition of the optimal solution which can significantly reduce the feasible optimal solution space without loss of performance. By modelling a finite-state Markov decision process (MDP) problem, we can numerically search an asymptotic periodic schedule which is proven to be optimal. Some simple but effective suboptimal schedules for any systems are proposed. We also find a lower bound of the optimal cost, which enables us to quantify the performance gap between any suboptimal schedule and an optimal one. [ABSTRACT FROM AUTHOR] |
| Copyright of Automatica is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 120226326 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimal sensor scheduling for multiple linear dynamical systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Han%2C+Duo%22">Han, Duo</searchLink><relatesTo>1,2</relatesTo><i> dhanaa@ntu.edu.sg</i><br /><searchLink fieldCode="AR" term="%22Wu%2C+Junfeng%22">Wu, Junfeng</searchLink><relatesTo>3</relatesTo><i> junfengw@kth.se</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Huanshui%22">Zhang, Huanshui</searchLink><relatesTo>4</relatesTo><i> hszhang@sdu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Shi%2C+Ling%22">Shi, Ling</searchLink><relatesTo>2</relatesTo><i> eesling@ust.hk</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Automatica%22">Automatica</searchLink>. Jan2017, Vol. 75, p260-270. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Markov+processes%22">Markov processes</searchLink><br /><searchLink fieldCode="DE" term="%22Dynamical+systems%22">Dynamical systems</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Optimal+control+theory%22">Optimal control theory</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We consider the design of an optimal collision-free sensor schedule for a number of sensors which monitor different linear dynamical systems correspondingly. At each time, only one of all the sensors can send its local estimate to the remote estimator. A preliminary work for the two-sensor scheduling case has been studied in the literature. The generalization into multiple-sensor scheduling case is shown to be nontrivial. We first find a necessary condition of the optimal solution which can significantly reduce the feasible optimal solution space without loss of performance. By modelling a finite-state Markov decision process (MDP) problem, we can numerically search an asymptotic periodic schedule which is proven to be optimal. Some simple but effective suboptimal schedules for any systems are proposed. We also find a lower bound of the optimal cost, which enables us to quantify the performance gap between any suboptimal schedule and an optimal one. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Automatica is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.automatica.2016.09.015 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 260 Subjects: – SubjectFull: Markov processes Type: general – SubjectFull: Dynamical systems Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Optimal control theory Type: general – SubjectFull: Kalman filtering Type: general Titles: – TitleFull: Optimal sensor scheduling for multiple linear dynamical systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Han, Duo – PersonEntity: Name: NameFull: Wu, Junfeng – PersonEntity: Name: NameFull: Zhang, Huanshui – PersonEntity: Name: NameFull: Shi, Ling IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 00051098 Numbering: – Type: volume Value: 75 Titles: – TitleFull: Automatica Type: main |
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