An efficient off-line formulation of robust model predictive control using linear matrix inequalities

Zhaoyang Wan, Mayuresh V. Kothare

Research output: Contribution to journalArticle

Abstract

The practicality of model predictive control (MPC) is partially limited by its ability to solve optimization problems in real time. Moreover, on-line computational demand for synthesizing a robust MPC algorithm will likely grow significantly with the problem size. In this paper, we use the concept of an asymptotically stable invariant ellipsoid to develop a robust constrained MPC algorithm which gives a sequence of explicit control laws corresponding to a sequence of asymptotically stable invariant ellipsoids constructed off-line one within another in state space. This off-line approach can address a broad class of model uncertainty descriptions with guaranteed robust stability of the closed-loop system and substantial reduction of the on-line MPC computation. The controller design is illustrated with two examples.

Original languageEnglish (US)
Pages (from-to)837-846
Number of pages10
JournalAutomatica
Volume39
Issue number5
DOIs
StatePublished - May 2003
Externally publishedYes

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Keywords

  • Asymptotic stability
  • Invariant ellipsoid
  • Linear matrix inequalities
  • Model predictive control
  • Multivariable constrained systems
  • On-line computation
  • Robust stability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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