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Fast, large-scale model predictive control by partial enumeration |
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| Authors: | Gabriele Pannocchia [Author Vitae] [Author Vitae] Stephen J Wright [Author Vitae] |
| Affiliation: | a Department of Chemical Engineering, Industrial Chemistry and Science of Materials, University of Pisa, Via Diotisalvi, 2, Pisa 56126, Italy b Department of Chemical and Biological Engineering, University of Wisconsin, 1415 Engineering Drive, Madison, WI 53706, USA c Computer Sciences Department, University of Wisconsin, 1210 West Dayton Street, Madison, WI 53706, USA |
| 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. |
| Keywords: | Model predictive control On-line optimization Large-scale systems |
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