Brief paper: Fast, large-scale model predictive control by partial enumeration

  • Authors:

  • Gabriele Pannocchia;James B. Rawlings;Stephen J. Wright

  • Affiliations:

  • Department of Chemical Engineering, Industrial Chemistry and Science of Materials, University of Pisa, Via Diotisalvi, 2, Pisa 56126, Italy;Department of Chemical and Biological Engineering, University of Wisconsin, 1415 Engineering Drive, Madison, WI 53706, USA;Computer Sciences Department, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2007

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Abstract

Partial enumeration (PE) is presented as a method for treating large, linear model predictive control applications that are out of reach with available MPC methods. PE uses both a table storage method and online optimization to achieve this goal. Versions of PE are shown to be closed-loop stable. PE is applied to an industrial example with more than 250 states, 32 inputs, and a 25-sample control horizon. The performance is less than 0.01% suboptimal, with average speedup factors in the range of 80-220, and worst-case speedups in the range of 4.9-39.2, compared to an existing MPC method. Small tables with only 25-200 entries were used to obtain this performance, while full enumeration is intractable for this example.