Model Predictive Control for Uncertain Fluid Processing Networks

Abstract

We discuss model predictive control and adjustable robust policies to control uncertain fluid processing networks in continuous time. We formulate this as a separated continuous time linear program (SCLP), with budgeted uncertainty of processing rates and a simple box uncertainty of arrival rates, similar to previous work by Bertsimas et al, and use a simplex type algorithm to solve it. We define an adjustable robust policy that changes according to actual server load and uses updates of buffer states at predefined times. We compare this to non-adjustable robust policy. The main feature of the implementation is that the simplex type algorithm for SCLP enables fast recalculation of updates.