Model predictive control of nonlinear systems using piecewise linear models

Leyla Özkan, Mayuresh V. Kothare, Christos Georgakis

Research output: Contribution to journalArticlepeer-review

46 Scopus citations


We consider the problem of controlling nonlinear systems which are modeled as a set of piecewise linear (PL) or affine systems using model predictive control (MPC). The paper reviews recent results on the analysis and control of PL systems, which can model a wide range of practically relevant nonlinear systems. Using techniques from the theory of linear matrix inequalities (LMIs), we develop a multiple model MPC technique involving a sequence of local state feedback matrices, which minimize an upper bound on the 'worst-case' objective function. The resulting problem, which utilizes a single quadratic Lyapunov function and multiple local state-feedback matrices, can be cast as a convex optimization problem involving LMIs. Several extensions of this technique involving approximating the local regions by ellipsoids or polytopes, and their respective advantages and disadvantages, are discussed. (C) 2000 Elsevier Science Ltd.

Original languageEnglish (US)
Pages (from-to)793-799
Number of pages7
JournalComputers and Chemical Engineering
Issue number2-7
StatePublished - Jul 15 2000
Externally publishedYes


  • Linear matrix inequalities
  • Nonlinear systems
  • Piecewise linear models

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Control and Systems Engineering


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