Approximation techniques for dynamic real-time optimization (DRTO) of distributed MPC systems

Abstract A dynamic real-time optimization (DRTO) strategy has recently been proposed for coordination of distributed MPC systems. The scheme utilizes a prediction model that accounts for the impact of the distributed controllers on the plant response. Two techniques are presented for approximating the closed-loop prediction within the DRTO formulation - a hybrid closed-loop and an input clipping formulation. The hybrid formulation generates closed-loop predictions for a limited number of time intervals along the DRTO prediction horizon, followed by an open-loop optimal control formulation for the rest of the horizon. The input clipping formulation utilizes an unconstrained MPC optimization formulation for each distributed MPC, coupled with an input saturation mechanism. The performance of the approximation techniques is evaluated through application to case studies based on linear and nonlinear dynamic plant models respectively. The approximation techniques are demonstrated to be more computationally efficient than the rigorous counterpart without significant loss in performance.

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