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.
|Title of host publication||2017 SICE International Symposium on Control Systems|
|Number of pages||8|
|Publication status||Published - 3 Apr 2017|
|Event||2017 SICE International Symposium on Control Systems - Okayama, Japan|
Duration: 6 Mar 2017 → 9 Mar 2017
|Conference||2017 SICE International Symposium on Control Systems|
|Abbreviated title||SICE ISCS 2017|
|Period||6/03/17 → 9/03/17|
- Sampling jitter, Particle filtering
El Hakim, V. S., & Bekooij, M. J. G. (2017). Sampling Jitter mitigation in latency-critical state-estimation applications using particle filters. In 2017 SICE International Symposium on Control Systems IEEE.