Event-Based State Estimation Using an Improved Stochastic Send-on-Delta Sampling Scheme
Thelander Andrén, Marcus LU and Cervin, Anton LU
(2016)
Event-Based Control, Communication and Signal Processing 2016
- Abstract
- Event-based sensing and communication holds the promise of lower resource utilization and/or better performance for remote state estimation applications found in e.g. networked control systems. Recently, stochastic event-triggering rules have been proposed as a means to avoid the complexity of the problem that normally arises in event-based estimator design. By using a scaled Gaussian function in the stochastic triggering scheme, the optimal remote state estimator becomes a linear Kalman filter with a case dependent measurement update. In this paper we propose a modified version of the stochastic send-on-delta triggering rule. The idea is to use a very simple predictor in the sensor, which allows the communication rate to be reduced while... (More)
- Event-based sensing and communication holds the promise of lower resource utilization and/or better performance for remote state estimation applications found in e.g. networked control systems. Recently, stochastic event-triggering rules have been proposed as a means to avoid the complexity of the problem that normally arises in event-based estimator design. By using a scaled Gaussian function in the stochastic triggering scheme, the optimal remote state estimator becomes a linear Kalman filter with a case dependent measurement update. In this paper we propose a modified version of the stochastic send-on-delta triggering rule. The idea is to use a very simple predictor in the sensor, which allows the communication rate to be reduced while preserving estimation performance compared to regular stochastic send-on-delta sampling. We derive the optimal mean-square error estimator for the new scheme and present upper and lower bounds on the error covariance. The proposed scheme is evaluated in numerical examples, where it compares favorably to previous stochastic sampling approaches, and is shown to preserve estimation performance well even at large reductions in communication rate. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/01d94738-9c3b-47d3-b020-a851ad2b8fd4
- author
- Thelander Andrén, Marcus
LU
and Cervin, Anton
LU
- organization
- publishing date
- 2016-06-13
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- remote state estimator, stochastic triggering, send-on-delta sampling, minimal mean square error estimator, event-based estimation
- host publication
- 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP)
- pages
- 8 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- Event-Based Control, Communication and Signal Processing 2016
- conference location
- Kraków, Poland
- conference dates
- 2016-06-13 - 2016-06-15
- external identifiers
-
- scopus:84998704823
- wos:000386662400015
- ISBN
- 978-1-5090-4196-1
- DOI
- 10.1109/EBCCSP.2016.7605237
- project
- ELLIIT LU P02: Co-Design of Robust and Secure Networked Embedded Control Systems
- Event-Based Control of Stochastic Systems with Application to Server Systems
- language
- English
- LU publication?
- yes
- id
- 01d94738-9c3b-47d3-b020-a851ad2b8fd4
- date added to LUP
- 2016-08-15 14:40:11
- date last changed
- 2024-02-03 07:17:38
@inproceedings{01d94738-9c3b-47d3-b020-a851ad2b8fd4,
abstract = {{Event-based sensing and communication holds the promise of lower resource utilization and/or better performance for remote state estimation applications found in e.g. networked control systems. Recently, stochastic event-triggering rules have been proposed as a means to avoid the complexity of the problem that normally arises in event-based estimator design. By using a scaled Gaussian function in the stochastic triggering scheme, the optimal remote state estimator becomes a linear Kalman filter with a case dependent measurement update. In this paper we propose a modified version of the stochastic send-on-delta triggering rule. The idea is to use a very simple predictor in the sensor, which allows the communication rate to be reduced while preserving estimation performance compared to regular stochastic send-on-delta sampling. We derive the optimal mean-square error estimator for the new scheme and present upper and lower bounds on the error covariance. The proposed scheme is evaluated in numerical examples, where it compares favorably to previous stochastic sampling approaches, and is shown to preserve estimation performance well even at large reductions in communication rate.}},
author = {{Thelander Andrén, Marcus and Cervin, Anton}},
booktitle = {{2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP)}},
isbn = {{978-1-5090-4196-1}},
keywords = {{remote state estimator; stochastic triggering; send-on-delta sampling; minimal mean square error estimator; event-based estimation}},
language = {{eng}},
month = {{06}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Event-Based State Estimation Using an Improved Stochastic Send-on-Delta Sampling Scheme}},
url = {{https://lup.lub.lu.se/search/files/18480516/EBCCSP_2016_submission_22.pdf}},
doi = {{10.1109/EBCCSP.2016.7605237}},
year = {{2016}},
}