Brief paper: Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

  • Authors:

  • Mark Cannon;Basil Kouvaritakis;Xingjian Wu

  • Affiliations:

  • Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, United Kingdom;Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, United Kingdom;Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, United Kingdom

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2009

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Abstract

Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p.