Brief Nonlinear model predictive control with polytopic invariant sets

  • Authors:
  • M. Cannon;V. Deshmukh;B. Kouvaritakis

  • Affiliations:
  • Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK;Mechanical Engineering Department, University of Alaska Fairbanks, Fairbanks, AK 99775, USA;Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, UK

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2003

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Abstract

Ellipsoidal invariant sets have been widely used as target sets in model predictive control (MPC). These sets can be computed by constructing appropriate linear difference inclusions together with additional constraints to ensure that the ellipsoid lies within a given inclusion polytope. The choice of inclusion polytope has a significant effect on the size of the target ellipsoid, but the optimal inclusion polytope cannot in general be computed systematically. This paper shows that use of polytopic invariant sets overcomes this difficulty, allowing larger stabilizable sets without loss of performance. In the interests of online efficiency, consideration is focused on interpolation-based MPC.