Brief paper: On the computation of linear model predictive control laws

  • Authors:
  • Francesco Borrelli;Mato Baotić;Jaroslav Pekar;Greg Stewart

  • Affiliations:
  • Department of Mechanical Engineering, University of California, Berkeley, 94720-1740, USA;Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia;Honeywell Prague Laboratory, V Parku 2326/18, 14800 Prague 4, Czech Republic;Honeywell Automation and Control Solutions, North Vancouver, BC V7J 3S4, Canada

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2010

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Abstract

Finite-time optimal control problems with quadratic performance index for linear systems with linear constraints can be transformed into Quadratic Programs (QPs). Model Predictive Control requires the on-line solution of such QPs. This can be obtained by using a QP solver or evaluating the associated explicit solution. The objective of this note is twofold. First, we shed some light on the computational complexity and storage demand of the two approaches when an active set QP solver is used. Second, we show the existence of alternative algorithms with a different tradeoff between memory and computational time. In particular, we present an algorithm which, for a certain class of systems, outperforms standard explicit solvers both in terms of memory and worst case computational time.