Advanced

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:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
keywords
remote state estimator, stochastic triggering, send-on-delta sampling, minimal mean square error estimator, event-based estimation
pages
8 pages
conference name
Event-Based Control, Communication and Signal Processing 2016
language
English
LU publication?
yes
id
01d94738-9c3b-47d3-b020-a851ad2b8fd4
date added to LUP
2016-08-15 14:40:11
date last changed
2016-12-02 14:42:19
@misc{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},
  keyword      = {remote state estimator,stochastic triggering,send-on-delta sampling,minimal mean square error estimator,event-based estimation},
  language     = {eng},
  month        = {06},
  pages        = {8},
  title        = {Event-Based State Estimation Using an Improved Stochastic Send-on-Delta Sampling Scheme},
  year         = {2016},
}