Iterated Square Root Unscented Kalman Filter for state estimation - CSTR model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this study, the use of improved Unscented Kalman Filter algorithm based on iterated measurement updates is proposed in an attempt to estimate the nonlinear and non-Gaussian state variables (the concentration and temperature) of the Continuously Stirred Tank Reactor (CSTR) process. Various conventional and state-of-the-art state estimation techniques are compared based on their estimation performance on this objective. These techniques are the Unscented Kalman Filter (UKF), the Square-Root Unscented Kalman Filter (SRUKF), the Iterated Unscented Kalman Filter (IUKF) and the developed Iterated Square Root Unscented Kalman Filter (ISRUKF). The results of the study indicate that the ISRUKF technique has better convergence properties than the IUKF technique; and both of them can provide improved accuracy over the UKF and SRUKF techniques. Moreover, ISRUKF technique is able to provide accuracy related advantages over other estimation techniques. Since this approach re-linearizes the measurement equation by iterating an approximate maximum a posteriori (MAP) estimate around the updated state, instead of relying on the predicted state.

Original languageEnglish
Title of host publication12th International Multi-Conference on Systems, Signals and Devices, SSD 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479917587
DOIs
Publication statusPublished - 4 Dec 2015
Event12th International Multi-Conference on Systems, Signals and Devices, SSD 2015 - Mahdia, Tunisia
Duration: 16 Mar 201519 Mar 2015

Other

Other12th International Multi-Conference on Systems, Signals and Devices, SSD 2015
CountryTunisia
CityMahdia
Period16/3/1519/3/15

Fingerprint

State estimation
Kalman filters

Keywords

  • Continuously Stirred Tank Reactor
  • Iterated Square Root
  • State Estimation
  • Unscented Kalman Filter

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications
  • Control and Systems Engineering

Cite this

Mansouri, M., Avci, O., Nounou, H., & Nounou, M. (2015). Iterated Square Root Unscented Kalman Filter for state estimation - CSTR model. In 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015 [7348243] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD.2015.7348243

Iterated Square Root Unscented Kalman Filter for state estimation - CSTR model. / Mansouri, Majdi; Avci, Onur; Nounou, Hazem; Nounou, Mohamed.

12th International Multi-Conference on Systems, Signals and Devices, SSD 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7348243.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Mansouri, M, Avci, O, Nounou, H & Nounou, M 2015, Iterated Square Root Unscented Kalman Filter for state estimation - CSTR model. in 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015., 7348243, Institute of Electrical and Electronics Engineers Inc., 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015, Mahdia, Tunisia, 16/3/15. https://doi.org/10.1109/SSD.2015.7348243
Mansouri M, Avci O, Nounou H, Nounou M. Iterated Square Root Unscented Kalman Filter for state estimation - CSTR model. In 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015. Institute of Electrical and Electronics Engineers Inc. 2015. 7348243 https://doi.org/10.1109/SSD.2015.7348243
Mansouri, Majdi ; Avci, Onur ; Nounou, Hazem ; Nounou, Mohamed. / Iterated Square Root Unscented Kalman Filter for state estimation - CSTR model. 12th International Multi-Conference on Systems, Signals and Devices, SSD 2015. Institute of Electrical and Electronics Engineers Inc., 2015.
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