Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks
The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distrib...
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| Autor*in: |
You, Jie [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on control systems technology - New York, NY : IEEE, 1993, 25(2017), 6, Seite 2151-2159 |
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| Übergeordnetes Werk: |
volume:25 ; year:2017 ; number:6 ; pages:2151-2159 |
| Links: |
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| DOI / URN: |
10.1109/TCST.2016.2638678 |
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| 520 | |a The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. | ||
| 650 | 4 | |a parameter identification | |
| 650 | 4 | |a Sensors | |
| 650 | 4 | |a Distributed parameter systems (DPSs) | |
| 650 | 4 | |a Distributed parameter systems | |
| 650 | 4 | |a Parameter estimation | |
| 650 | 4 | |a Kalman filters | |
| 650 | 4 | |a Mobile communication | |
| 650 | 4 | |a mobile sensor networks | |
| 650 | 4 | |a Trajectory | |
| 700 | 1 | |a Wu, Wencen |4 oth | |
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10.1109/TCST.2016.2638678 doi PQ20171228 (DE-627)OLC1998272370 (DE-599)GBVOLC1998272370 (PRQ)c1310-529193f4c7fe4fa4637cb2161167309a45c1f44c774e1479401f583af1f510580 (KEY)0226256820170000025000602151onlinepassiveidentifierforspatiallydistributedsyst DE-627 ger DE-627 rakwb eng 004 DNB You, Jie verfasserin aut Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. parameter identification Sensors Distributed parameter systems (DPSs) Distributed parameter systems Parameter estimation Kalman filters Mobile communication mobile sensor networks Trajectory Wu, Wencen oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 25(2017), 6, Seite 2151-2159 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:25 year:2017 number:6 pages:2151-2159 http://dx.doi.org/10.1109/TCST.2016.2638678 Volltext http://ieeexplore.ieee.org/document/7812666 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2016 AR 25 2017 6 2151-2159 |
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10.1109/TCST.2016.2638678 doi PQ20171228 (DE-627)OLC1998272370 (DE-599)GBVOLC1998272370 (PRQ)c1310-529193f4c7fe4fa4637cb2161167309a45c1f44c774e1479401f583af1f510580 (KEY)0226256820170000025000602151onlinepassiveidentifierforspatiallydistributedsyst DE-627 ger DE-627 rakwb eng 004 DNB You, Jie verfasserin aut Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. parameter identification Sensors Distributed parameter systems (DPSs) Distributed parameter systems Parameter estimation Kalman filters Mobile communication mobile sensor networks Trajectory Wu, Wencen oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 25(2017), 6, Seite 2151-2159 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:25 year:2017 number:6 pages:2151-2159 http://dx.doi.org/10.1109/TCST.2016.2638678 Volltext http://ieeexplore.ieee.org/document/7812666 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2016 AR 25 2017 6 2151-2159 |
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10.1109/TCST.2016.2638678 doi PQ20171228 (DE-627)OLC1998272370 (DE-599)GBVOLC1998272370 (PRQ)c1310-529193f4c7fe4fa4637cb2161167309a45c1f44c774e1479401f583af1f510580 (KEY)0226256820170000025000602151onlinepassiveidentifierforspatiallydistributedsyst DE-627 ger DE-627 rakwb eng 004 DNB You, Jie verfasserin aut Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. parameter identification Sensors Distributed parameter systems (DPSs) Distributed parameter systems Parameter estimation Kalman filters Mobile communication mobile sensor networks Trajectory Wu, Wencen oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 25(2017), 6, Seite 2151-2159 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:25 year:2017 number:6 pages:2151-2159 http://dx.doi.org/10.1109/TCST.2016.2638678 Volltext http://ieeexplore.ieee.org/document/7812666 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2016 AR 25 2017 6 2151-2159 |
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10.1109/TCST.2016.2638678 doi PQ20171228 (DE-627)OLC1998272370 (DE-599)GBVOLC1998272370 (PRQ)c1310-529193f4c7fe4fa4637cb2161167309a45c1f44c774e1479401f583af1f510580 (KEY)0226256820170000025000602151onlinepassiveidentifierforspatiallydistributedsyst DE-627 ger DE-627 rakwb eng 004 DNB You, Jie verfasserin aut Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. parameter identification Sensors Distributed parameter systems (DPSs) Distributed parameter systems Parameter estimation Kalman filters Mobile communication mobile sensor networks Trajectory Wu, Wencen oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 25(2017), 6, Seite 2151-2159 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:25 year:2017 number:6 pages:2151-2159 http://dx.doi.org/10.1109/TCST.2016.2638678 Volltext http://ieeexplore.ieee.org/document/7812666 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2016 AR 25 2017 6 2151-2159 |
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10.1109/TCST.2016.2638678 doi PQ20171228 (DE-627)OLC1998272370 (DE-599)GBVOLC1998272370 (PRQ)c1310-529193f4c7fe4fa4637cb2161167309a45c1f44c774e1479401f583af1f510580 (KEY)0226256820170000025000602151onlinepassiveidentifierforspatiallydistributedsyst DE-627 ger DE-627 rakwb eng 004 DNB You, Jie verfasserin aut Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. parameter identification Sensors Distributed parameter systems (DPSs) Distributed parameter systems Parameter estimation Kalman filters Mobile communication mobile sensor networks Trajectory Wu, Wencen oth Enthalten in IEEE transactions on control systems technology New York, NY : IEEE, 1993 25(2017), 6, Seite 2151-2159 (DE-627)171098137 (DE-600)1151354-8 (DE-576)03420315X 1063-6536 nnns volume:25 year:2017 number:6 pages:2151-2159 http://dx.doi.org/10.1109/TCST.2016.2638678 Volltext http://ieeexplore.ieee.org/document/7812666 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2016 AR 25 2017 6 2151-2159 |
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Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks |
| abstract |
The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. |
| abstractGer |
The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. |
| abstract_unstemmed |
The problem of online parameter identification for spatially distributed systems using a mobile sensor network is investigated in this brief. Inspired by the passive identifier developed for boundary control of partial differential equations (PDEs) using a static sensor network, we propose a distributed online passive identifier that is able to estimate the unknown diffusion coefficient of the advection-diffusion PDE using data collected by a mobile sensor network moving in the field. To enable the online passive identifier, we develop a distributed cooperative Kalman filter run by each sensing agent that provides state estimates of the field. Optimal trajectory for the mobile sensor network in the advection-diffusion field is found, and distributed control laws to control the mobile sensor network to estimate and follow the trajectory while keeping a desired formation are designed. We prove that the parameter estimation errors are bounded and achieve parameter consensus. In addition, by generating a persistence of excitation condition, we further verify the asymptotic parameter convergence. Numerical simulations for 2-D and 3-D cases demonstrate the effectiveness of the proposed online passive identifier. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2016 |
| container_issue |
6 |
| title_short |
Online Passive Identifier for Spatially Distributed Systems Using Mobile Sensor Networks |
| url |
http://dx.doi.org/10.1109/TCST.2016.2638678 http://ieeexplore.ieee.org/document/7812666 |
| remote_bool |
false |
| author2 |
Wu, Wencen |
| author2Str |
Wu, Wencen |
| ppnlink |
171098137 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth |
| doi_str |
10.1109/TCST.2016.2638678 |
| up_date |
2024-07-04T04:40:29.015Z |
| _version_ |
1803622053587189760 |
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| score |
7.401457 |