Economic Coordination of Distributed Nonlinear MPC Systems using Closed-loop Prediction of a Nonlinear Dynamic Plant ⁎ ⁎ This work is sponsored by the McMaster Advanced Control Consortium (MACC) and the Imperial Oil University Research Award.

A coordination scheme for nonlinear MPCs is presented using a dynamic real-time optimization (DRTO) formulation with a nonlinear dynamic plant model. By considering the control action of constrained nonlinear MPCs, the nonlinear DRTO formulation generates the predicted closed-loop response of the plant and computes optimal set-point trajectories based on an economic objective. The set-point trajectories are assigned to lower-level nonlinear MPCs for tracking. Due to the inclusion of nonlinear MPC regulation, the DRTO formulation results in a multi-level optimization problem. The solution strategy applied is to transform the nonlinear MPC optimization subproblems into sets of algebraic equations using the Karush-Kuhn-Tucker (KKT) optimality conditions, and to embed these equations in the DRTO formulation to yield a single-level optimization problem. The performance of proposed formulation is evaluated through application to a case study, with comparisons made against its counterpart that utilizes linear DRTO and MPC formulations.