Periodic Event-Triggered Estimation for Networked Control Systems
This paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote esti...
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doaj-164049610fa743c9ac52d996be69905b2021-09-26T00:03:08ZengMDPI AGElectronics2079-92922021-09-01102215221510.3390/electronics10182215Periodic Event-Triggered Estimation for Networked Control SystemsShitong Cui0Le Liu1Wei Xing2Xudong Zhao3The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education, Dalian University of Technology, Dalian 116000, ChinaThe Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education, Dalian University of Technology, Dalian 116000, ChinaThe Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education, Dalian University of Technology, Dalian 116000, ChinaThe Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education, Dalian University of Technology, Dalian 116000, ChinaThis paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote estimator and the smart sensor is activated, the remote estimator simply adopts the estimate transmitted by the smart sensor. Otherwise, it calculates an estimate based on the available information. The closed-form of the minimum mean-square error (MMSE) estimator is introduced, and we use Gaussian preserving event-based sensor scheduling to obtain an ideal compromise between the communication cost and estimation quality. Furthermore, we calculate a variation range of communication probability, which helps to design the policy of event-triggered estimation. Finally, the simulation results are given to illustrate the effectiveness of the proposed event-triggered estimator.https://www.mdpi.com/2079-9292/10/18/2215networked control systemsevent-triggeredestimationKalman filtering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shitong Cui Le Liu Wei Xing Xudong Zhao |
spellingShingle |
Shitong Cui Le Liu Wei Xing Xudong Zhao Periodic Event-Triggered Estimation for Networked Control Systems Electronics networked control systems event-triggered estimation Kalman filtering |
author_facet |
Shitong Cui Le Liu Wei Xing Xudong Zhao |
author_sort |
Shitong Cui |
title |
Periodic Event-Triggered Estimation for Networked Control Systems |
title_short |
Periodic Event-Triggered Estimation for Networked Control Systems |
title_full |
Periodic Event-Triggered Estimation for Networked Control Systems |
title_fullStr |
Periodic Event-Triggered Estimation for Networked Control Systems |
title_full_unstemmed |
Periodic Event-Triggered Estimation for Networked Control Systems |
title_sort |
periodic event-triggered estimation for networked control systems |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-09-01 |
description |
This paper considers the problem of remote state estimation in a linear discrete invariant system, where a smart sensor is utilized to measure the system state and generate a local estimate. The communication depends on an event scheduler in the smart sensor. When the channel between the remote estimator and the smart sensor is activated, the remote estimator simply adopts the estimate transmitted by the smart sensor. Otherwise, it calculates an estimate based on the available information. The closed-form of the minimum mean-square error (MMSE) estimator is introduced, and we use Gaussian preserving event-based sensor scheduling to obtain an ideal compromise between the communication cost and estimation quality. Furthermore, we calculate a variation range of communication probability, which helps to design the policy of event-triggered estimation. Finally, the simulation results are given to illustrate the effectiveness of the proposed event-triggered estimator. |
topic |
networked control systems event-triggered estimation Kalman filtering |
url |
https://www.mdpi.com/2079-9292/10/18/2215 |
work_keys_str_mv |
AT shitongcui periodiceventtriggeredestimationfornetworkedcontrolsystems AT leliu periodiceventtriggeredestimationfornetworkedcontrolsystems AT weixing periodiceventtriggeredestimationfornetworkedcontrolsystems AT xudongzhao periodiceventtriggeredestimationfornetworkedcontrolsystems |
_version_ |
1717367184302276608 |