Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters

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Abstract

State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.
Original languageEnglish
Title of host publication2017 SICE International Symposium on Control Systems
PublisherIEEE
Number of pages8
ISBN (Electronic)978-4-907764-54-8
ISBN (Print)978-1-5090-5614-9
Publication statusPublished - 3 Apr 2017
Event2017 SICE International Symposium on Control Systems - Okayama, Japan
Duration: 6 Mar 20179 Mar 2017

Conference

Conference2017 SICE International Symposium on Control Systems
Abbreviated titleSICE ISCS 2017
CountryJapan
CityOkayama
Period6/03/179/03/17

Fingerprint

Critical State
Jitter
Particle Filter
State Estimation
State estimation
Latency
Sampling
Execution Time
Kalman filters
Kalman Filter
DC motors
Sensor Fusion
Digital Control
DC Motor
Error analysis
Multiprocessor Systems
Estimation Error
Estimation Algorithms
Fusion reactions
Conditioning

Keywords

  • Sampling jitter, Particle filtering

Cite this

@inproceedings{756978869982462294817b9007e0e938,
title = "Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters",
abstract = "State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.",
keywords = "Sampling jitter, Particle filtering",
author = "{El Hakim}, {Viktorio Semir} and Bekooij, {Marco Jan Gerrit}",
year = "2017",
month = "4",
day = "3",
language = "English",
isbn = "978-1-5090-5614-9",
booktitle = "2017 SICE International Symposium on Control Systems",
publisher = "IEEE",
address = "United States",

}

El Hakim, VS & Bekooij, MJG 2017, Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters. in 2017 SICE International Symposium on Control Systems. IEEE, 2017 SICE International Symposium on Control Systems, Okayama, Japan, 6/03/17.

Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters. / El Hakim, Viktorio Semir; Bekooij, Marco Jan Gerrit.

2017 SICE International Symposium on Control Systems. IEEE, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

TY - GEN

T1 - Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters

AU - El Hakim, Viktorio Semir

AU - Bekooij, Marco Jan Gerrit

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N2 - State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.

AB - State estimation algorithms, such as the Kalman filter, are applied for conditioning and sensor fusion in digital control loops. It is desirable that these algorithms can be executed on embedded multiprocessor systems. However this results in large worst-case execution times with a consequence that a large sampling period must selected, which degrades the estimation and control performance. In this paper, we propose a free-running state-estimation approach in which the next sample is taken as soon as the current iteration completes. The approach utilizes the particle filter algorithm to mitigate the effects of sampling jitter, introduced by the variation in the execution times of tasks. As a result of the reduced interval between subsequent sampling moments the estimation accuracy is improved. The delay introduced by the estimator in a control loop is reduced by enabling execution of the prediction step in parallel with other control tasks. We compare simulation results obtained for our approach with a Kalman filter based approach, by estimating the state of a DC motor. These results show that our approach minimizes the estimation error, as a result of sampling jitter, by up to a factor of 10. Additionally we show that the approach does not require precise knowledge of the distribution of the execution times of the tasks.

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