Improved particle filtering for state and parameter estimation- CSTR model

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

1 Citation (Scopus)

Abstract

This paper addresses the problem of states and parameters estimation for a continuously stirred tank reactor using Bayesian methods. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the Particle Filter (PF), and the developed improved particle filter (IPF). Unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The proposal sampling distribution is obtained by minimizing the Kullback-Leibler divergence (KLD) distance. The simulation results show that the new improved particle filter superiors to the standard particle filter. In addition, IPF can still provide both convergence as well as accuracy related advantages over other estimation methods.

Original languageEnglish
Title of host publication2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014
PublisherIEEE Computer Society
DOIs
Publication statusPublished - 2014
Event2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014 - Castelldefels-Barcelona, Spain
Duration: 11 Feb 201414 Feb 2014

Other

Other2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014
CountrySpain
CityCastelldefels-Barcelona
Period11/2/1414/2/14

Fingerprint

State estimation
Parameter estimation
Sampling

Keywords

  • Continuously stirred tank reactor
  • Kullback-Leibler divergence
  • Parameter estimation
  • Particle filter
  • State estimation

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering

Cite this

Mansouri, M., Nounou, H., & Nounou, M. (2014). Improved particle filtering for state and parameter estimation- CSTR model. In 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014 [6808794] IEEE Computer Society. https://doi.org/10.1109/SSD.2014.6808794

Improved particle filtering for state and parameter estimation- CSTR model. / Mansouri, Majdi; Nounou, Hazem; Nounou, Mohamed.

2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014. IEEE Computer Society, 2014. 6808794.

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

Mansouri, M, Nounou, H & Nounou, M 2014, Improved particle filtering for state and parameter estimation- CSTR model. in 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014., 6808794, IEEE Computer Society, 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014, Castelldefels-Barcelona, Spain, 11/2/14. https://doi.org/10.1109/SSD.2014.6808794
Mansouri M, Nounou H, Nounou M. Improved particle filtering for state and parameter estimation- CSTR model. In 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014. IEEE Computer Society. 2014. 6808794 https://doi.org/10.1109/SSD.2014.6808794
Mansouri, Majdi ; Nounou, Hazem ; Nounou, Mohamed. / Improved particle filtering for state and parameter estimation- CSTR model. 2014 IEEE 11th International Multi-Conference on Systems, Signals and Devices, SSD 2014. IEEE Computer Society, 2014.
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