Coordination of Distributed MPC Systems via Dynamic Real-time Optimization **This work is sponsored by the McMaster Advanced Control Consortium (MACC) and the Ministry of Higher Education (MOHE), Malaysia

This paper focuses on the application of a dynamic real-time optimization (DRTO) formulation utilizing an approximation of plant closed-loop prediction for coordination of distributed model predictive control (MPC) systems. We formulate the DRTO problem as a bilevel program that embeds the optimization problems of all MPC controllers functioning in the process, hence computing the set-point trajectories for all controllers simultaneously. The process model used within the DRTO module is consistent with the dynamic models used in the MPC controllers, but with the interactions between the process subsystems captured through the impact of local control actions on the predicted plantwide closed-loop response dynamics. The MPC optimization subproblems embedded in the closed-loop DRTO formulation are subsequently replaced by their first-order Karush-Kuhn-Tucker (KKT) optimality conditions to yield a single-level mathematical program with complementarity constraints (MPCC). The performance of the proposed approach is assessed via case study simulations involving an economic coordination scheme.