A gradient-based strategy for the one-layer RTO+MPC controller

In the process industries model predictive controllers (MPC) have the task of controlling the plant ensuring stability and constraints satisfaction, while an economic cost is minimized. Usually the economic objective is optimized by an upper level Real Time Optimizer (RTO) that passes the economically optimal setpoints to the MPC level. The drawback of this structure is the possible inconsistence/unreachability of those setpoints, due to the different models employed by the RTO and the MPC, as well as their different time scales. In this paper an MPC that explicitly integrates the RTO structure into the dynamic control layer is presented. To overcome the complexity of this one-layer formulation a gradient-based approximation is proposed, which provides a low-computational-cost suboptimal solution.