Improving the performance of generalized predictive control for nonlinear processes

Mamoun Abu-Ayyad, Rickey Dubay

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

This paper presents a unique method for improving the performance of the generalized predictive control (GPC) algorithm for controlling nonlinear systems which can be extended to other forms of predictive controllers. This method is termed adaptive generalized predictive control (AGPC) which uses a multidimensional workspace of the nonlinear plant to recalculate the controller parameters every sampling instant. This results in a more accurate process prediction and improved closed-loop performance over the original GPC algorithm. The AGPC controller was tested in simulation, and its control performance was compared to GPC on several nonlinear plants with different degrees of nonlinearity. Practical testing and comparisons were performed on a steel cylinder temperature control system. Simulation and experimental results show that the adaptive generalized predictive controller provided improved closed-loop performance over GPC. The formulation of the multidimensional workspace can be readily applied to other advanced control strategies making the methodology generic.

Original languageEnglish (US)
Pages (from-to)4809-4816
Number of pages8
JournalIndustrial and Engineering Chemistry Research
Volume49
Issue number10
DOIs
StatePublished - May 19 2010

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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