An off-line model reduction-based technique for on-line linear MPC applications for nonlinear large-scale distributed systems

Weiguo Xie, Constantinos Theodoropoulos

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Linear Model Predictive Control (MPC) has been effectively applied for many process systems. However, linear MPC is often inappropriate for controlling nonlinear large-scale systems. To overcome this, model reduction methodology has been exploited to enable the efficient application of linear MPC for nonlinear distributed-parameter systems. An implementation of the proper orthogonal decomposition method combined with a finite element Galerkin projection is first used to extract accurate non-linear low-order models from the large-scale ones. Then a Trajectory Piecewise-Linear method is developed to construct a piecewise linear representation of the reduced nonlinear model. Linear MPC, based on quadratic programming, can then be efficiently performed on the resulting system. The stabilisation of the oscillatory behaviour of a tubular reactor with recycle is used as an illustrative example to demonstrate our methodology.

Original languageEnglish (US)
Pages (from-to)409-414
Number of pages6
JournalComputer Aided Chemical Engineering
Volume28
Issue numberC
DOIs
StatePublished - 2010
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like taoc knowledge the financial supporot f the EC FP6 Project: CONNECT [COOP-2006-31638] and the ECF P7 project CAFÉ[ KBBE-212754].

Keywords

  • Distributed systems
  • Model predictive control
  • Model reduction
  • Proper orthogonal decomposition
  • Trajectory piecewise-linear

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