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

• A one-layer control scheme is proposed, in which the RTO cost function is part of the MPC cost function.
• Due to the high nonlinearity of the RTO cost, a gradient-based approximation is considered.
• The applied solution is a convex combination of the approximated problem solution and a previously known feasible one.
• Convergence is proved, as well as decreasing of the original (nonlinear) cost.
• Stability is proved, by means of a Lyapunov function.

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.