Distributed Non-Fragile State Estimation for Uncertain Nonlinear Systems of Sensor Networks Subject to Sensor Nonlinearities
This paper studies the distributed state estimation issue of nonlinear dynamical systems with parameter uncertainties based on sensor networks under the non-fragile control framework. Moreover, all the sensors are in a fully distributed framework with information exchanges to reduce the communicatio...
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| Published in | Sensors (Basel, Switzerland) Vol. 25; no. 7; p. 1962 |
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| Format | Journal Article |
| Language | English |
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21.03.2025
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| Abstract | This paper studies the distributed state estimation issue of nonlinear dynamical systems with parameter uncertainties based on sensor networks under the non-fragile control framework. Moreover, all the sensors are in a fully distributed framework with information exchanges to reduce the communication and computation resources. In particular, the sensor nonlinearities in the sensor network and state estimation gain fluctuations are taken into account for more general applicability. With the help of the Lyapunov–Krasovskii approach, sufficient convex optimization criteria can be given so that the passivity performance of its resultant state estimation error system can be guaranteed. The optimized non-fragile state estimation gains can be further determined on the basis of solving the convex optimization. The advantages and usefulness of our developed results are finally demonstrated by two illustrative examples. |
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| AbstractList | This paper studies the distributed state estimation issue of nonlinear dynamical systems with parameter uncertainties based on sensor networks under the non-fragile control framework. Moreover, all the sensors are in a fully distributed framework with information exchanges to reduce the communication and computation resources. In particular, the sensor nonlinearities in the sensor network and state estimation gain fluctuations are taken into account for more general applicability. With the help of the Lyapunov–Krasovskii approach, sufficient convex optimization criteria can be given so that the passivity performance of its resultant state estimation error system can be guaranteed. The optimized non-fragile state estimation gains can be further determined on the basis of solving the convex optimization. The advantages and usefulness of our developed results are finally demonstrated by two illustrative examples. This paper studies the distributed state estimation issue of nonlinear dynamical systems with parameter uncertainties based on sensor networks under the non-fragile control framework. Moreover, all the sensors are in a fully distributed framework with information exchanges to reduce the communication and computation resources. In particular, the sensor nonlinearities in the sensor network and state estimation gain fluctuations are taken into account for more general applicability. With the help of the Lyapunov-Krasovskii approach, sufficient convex optimization criteria can be given so that the passivity performance of its resultant state estimation error system can be guaranteed. The optimized non-fragile state estimation gains can be further determined on the basis of solving the convex optimization. The advantages and usefulness of our developed results are finally demonstrated by two illustrative examples.This paper studies the distributed state estimation issue of nonlinear dynamical systems with parameter uncertainties based on sensor networks under the non-fragile control framework. Moreover, all the sensors are in a fully distributed framework with information exchanges to reduce the communication and computation resources. In particular, the sensor nonlinearities in the sensor network and state estimation gain fluctuations are taken into account for more general applicability. With the help of the Lyapunov-Krasovskii approach, sufficient convex optimization criteria can be given so that the passivity performance of its resultant state estimation error system can be guaranteed. The optimized non-fragile state estimation gains can be further determined on the basis of solving the convex optimization. The advantages and usefulness of our developed results are finally demonstrated by two illustrative examples. |
| Audience | Academic |
| Author | Xu, Ke Tian, Shihui Huang, Fengshan |
| AuthorAffiliation | 1 Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China; shtian@xs.ustb.edu.cn 2 College of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China |
| AuthorAffiliation_xml | – name: 1 Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China; shtian@xs.ustb.edu.cn – name: 2 College of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China |
| Author_xml | – sequence: 1 givenname: Shihui orcidid: 0000-0002-1631-1968 surname: Tian fullname: Tian, Shihui – sequence: 2 givenname: Ke orcidid: 0000-0003-1809-7413 surname: Xu fullname: Xu, Ke – sequence: 3 givenname: Fengshan surname: Huang fullname: Huang, Fengshan |
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| Cites_doi | 10.1016/j.isatra.2020.01.001 10.1109/JPROC.2003.814926 10.1049/iet-gtd.2016.0988 10.1109/TFUZZ.2010.2060203 10.1016/j.automatica.2021.110004 10.1145/3561512 10.1504/IJMR.2006.010700 10.1109/TNNLS.2015.2490168 10.1016/j.inffus.2005.06.003 10.1080/00207179.2017.1350884 10.1016/j.sigpro.2021.108150 10.1155/2020/9592836 10.1109/TSMC.2017.2754495 10.1016/j.jfranklin.2015.02.002 10.4304/jcm.6.2.143-151 10.1155/2012/962523 10.1109/TSP.2007.896099 10.1109/TIE.2009.2039455 10.1115/1.4023894 10.1016/j.neucom.2020.12.027 10.1016/j.automatica.2010.10.014 10.1080/00207721.2021.1872118 10.1016/S0024-3795(98)10123-4 10.1080/03081079.2014.883711 10.1109/TAC.2016.2593742 10.1080/03081079.2014.892250 10.1109/TFUZZ.2014.2367101 |
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| Title | Distributed Non-Fragile State Estimation for Uncertain Nonlinear Systems of Sensor Networks Subject to Sensor Nonlinearities |
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