Periodic learning of b-spline models for output PDF control: application to MWD control

H. Wang, Z.J. Zhang, H. Yue

Research output: Contribution to conferencePaper

10 Citations (Scopus)

Abstract

Periodic learning of B-spline basis functions model for the output probability density function (PDF) control of non-Gaussian systems is studied in this paper using the recursive least square algorithm. Within each control interval, the basis functions are fixed and the control input design is performed that controls the shape of the output PDFs. However, between each control interval, periodic learning techniques are used to tune the shape of the basis functions. This has been shown to be able to improve the accuracy of the B-spline approximation model. As such, the overall B-spline model of the output PDFs becomes a dual-model related to both time and space variables. The algorithm has been applied to a simulation study of the molecular weight distribution (MWD) control of a styrene polymerization process, leading to some interesting results.
LanguageEnglish
Pages955-960
Number of pages6
DOIs
Publication statusPublished - Jun 2005
EventAmerican Control Conference 2005 (ACC) - Portland, United States
Duration: 8 Jun 200510 Jun 2005

Conference

ConferenceAmerican Control Conference 2005 (ACC)
CountryUnited States
CityPortland
Period8/06/0510/06/05

Fingerprint

Molecular weight distribution
Splines
Probability density function
Styrene
Polymerization

Keywords

  • molecular-weight distribution
  • equation
  • distributions
  • emulsion polymerization
  • stochastic-systems
  • particle-size distribution
  • probability density-function

Cite this

Wang, H., Zhang, Z. J., & Yue, H. (2005). Periodic learning of b-spline models for output PDF control: application to MWD control. 955-960. Paper presented at American Control Conference 2005 (ACC) , Portland, United States. https://doi.org/10.1109/ACC.2005.1470083
Wang, H. ; Zhang, Z.J. ; Yue, H. / Periodic learning of b-spline models for output PDF control : application to MWD control. Paper presented at American Control Conference 2005 (ACC) , Portland, United States.6 p.
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author = "H. Wang and Z.J. Zhang and H. Yue",
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Wang, H, Zhang, ZJ & Yue, H 2005, 'Periodic learning of b-spline models for output PDF control: application to MWD control' Paper presented at American Control Conference 2005 (ACC) , Portland, United States, 8/06/05 - 10/06/05, pp. 955-960. https://doi.org/10.1109/ACC.2005.1470083

Periodic learning of b-spline models for output PDF control : application to MWD control. / Wang, H.; Zhang, Z.J.; Yue, H.

2005. 955-960 Paper presented at American Control Conference 2005 (ACC) , Portland, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Periodic learning of b-spline models for output PDF control

T2 - application to MWD control

AU - Wang, H.

AU - Zhang, Z.J.

AU - Yue, H.

PY - 2005/6

Y1 - 2005/6

N2 - Periodic learning of B-spline basis functions model for the output probability density function (PDF) control of non-Gaussian systems is studied in this paper using the recursive least square algorithm. Within each control interval, the basis functions are fixed and the control input design is performed that controls the shape of the output PDFs. However, between each control interval, periodic learning techniques are used to tune the shape of the basis functions. This has been shown to be able to improve the accuracy of the B-spline approximation model. As such, the overall B-spline model of the output PDFs becomes a dual-model related to both time and space variables. The algorithm has been applied to a simulation study of the molecular weight distribution (MWD) control of a styrene polymerization process, leading to some interesting results.

AB - Periodic learning of B-spline basis functions model for the output probability density function (PDF) control of non-Gaussian systems is studied in this paper using the recursive least square algorithm. Within each control interval, the basis functions are fixed and the control input design is performed that controls the shape of the output PDFs. However, between each control interval, periodic learning techniques are used to tune the shape of the basis functions. This has been shown to be able to improve the accuracy of the B-spline approximation model. As such, the overall B-spline model of the output PDFs becomes a dual-model related to both time and space variables. The algorithm has been applied to a simulation study of the molecular weight distribution (MWD) control of a styrene polymerization process, leading to some interesting results.

KW - molecular-weight distribution

KW - equation

KW - distributions

KW - emulsion polymerization

KW - stochastic-systems

KW - particle-size distribution

KW - probability density-function

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DO - 10.1109/ACC.2005.1470083

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Wang H, Zhang ZJ, Yue H. Periodic learning of b-spline models for output PDF control: application to MWD control. 2005. Paper presented at American Control Conference 2005 (ACC) , Portland, United States. https://doi.org/10.1109/ACC.2005.1470083