Fast, large-scale model predictive control by partial enumeration

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.