Control relevant model reduction of Volterra series models

Wei Ming Ling, Daniel Rivera

Research output: Contribution to journalArticle

29 Scopus citations

Abstract

This paper presents a two-step method for control-relevant model reduction of Volterra series models. First, using the nonlinear IMC design as a basis, an explicit expression relating the closed-loop performance to the open-loop modeling error is obtained. Secondly, an optimization problem that seeks to minimize the closed-loop error subject to the restriction of a reduced-order model is posed. By showing that model reduction of kernels with different degrees can be decoupled in the problem formulation, the optimization problem is simplified into a mathematically more convenient form which can be solved with significantly less computational effort. The effectiveness of the proposed method is illustrated on a polymerization reactor example where a second-order Volterra model with 85 parameters is reduced to a Hammerstein model with 3 parameters. Despite the lower 'open-loop' predictive ability of the control-relevant model, the closed-loop performance of the reduced-order control system closely mimics that of the full order model.

Original languageEnglish (US)
Pages (from-to)79-88
Number of pages10
JournalJournal of Process Control
Volume8
Issue number2
DOIs
StatePublished - Apr 1998

Keywords

  • Control-relevant modeling
  • Model reduction
  • Volterra series

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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