Explicit Model Predictive Control for a Highly Interacting System

Explicit Model Predictive Control (MPC) of highly interacting systems using multiparametric programming is challenging as the offline solution to the Optimal Control Problem (OCP) typically entails calculation of a large number of regions of the uncertainty space. This can result in the case in which the point location problem is computationally more expensive than solving the OCP online. Hence, in this paper, with an aim to reduce computational costs of explicit MPC, we reformulate the OCP and study the computational and control performance of the reformulated explicit MPC compared to the conventional explicit MPC. As a case study, we consider a highly interacting quadruple tank process. The closed-loop simulation results show that between conventional MPC and reformulated MPC, in the online case, the total computational times are comparable, whereas in the explicit MPC case, the reformulation results in significant reduction in the total computational time by 44%.