Implementations of a Hammerstein fuzzy-neural model for predictive control of a lyophilization plant

Yancho Todorov*, Sevil Ahmed, Michail Petrov, Vasilliy Chitanov

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

This paper describes two methodologies for implementation of Hammerstein model by using different input-output representations into model predictive control schemes. The model nonlinearity is easily approximated using a simple Takagi-Sugeno inference, while the linear parts are flexibly introduced. As optimization procedures for predictive control are used a standard gradient optimization method and an implementation of Hildreth Quadratic Programming. A comparison between the proposed control strategies is made by simulation experiments for control of nonlinear lyophilization plant.

Original languageEnglish
Title of host publicationIS'2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings
PublisherIEEE
Pages316-321
Number of pages6
ISBN (Print)9781467327824
DOIs
Publication statusPublished - 2012
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Intelligent Systems - Sofia, Bulgaria
Duration: 6 Sep 20128 Sep 2012
Conference number: 6

Conference

ConferenceIEEE International Conference on Intelligent Systems
Abbreviated titleIS
CountryBulgaria
CitySofia
Period06/09/201208/09/2012

Keywords

  • fuzzy-neural models
  • gradient descent
  • Hildreth quadratic programming
  • lyophilization
  • optimization
  • predictive control

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