A methodology for control-relevant nonlinear system identification using restricted complexity models

Wei Ming Ling, Daniel Rivera

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented. Control design based on RCMs often leads to controllers which are easy to interpret and implement in real-time. A control-relevant identification method is developed to minimize the degradation in closed-loop performance as a result of RCM approximation error. A two-stage identification procedure is presented. First, a nonlinear ARX model is estimated from plant data using an orthogonal least squares algorithm; a Volterra series model is then generated from the nonlinear ARX model. In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements. The effectiveness of the proposed method is illustrated using two chemical reactor examples.

Original languageEnglish (US)
Pages (from-to)209-222
Number of pages14
JournalJournal of Process Control
Volume11
Issue number2
DOIs
StatePublished - Apr 1 2001

Keywords

  • Control relevant modeling
  • Nonlinear systems
  • Reduced order models
  • System identification
  • Volterra series

ASJC Scopus subject areas

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

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