Optimization Target Resetting Distributed Model Predictive Control for accelerated cooling process

In accelerated cooling (ACC) processes-large scale systems, since the starting temperature varies along plate, the cooling curves of plate-points are different to each other for obtaining the uniform characteristic of the whole plate. To satisfy the complicated control targets--the various cooling curves, and real-time control requirement, a novel method called Optimization Target Resetting based Distributed Model Predictive Control (OTR-DMPC) is proposed. The whole system is divided into many subsystems, and each subsystem is controlled by a local Model Predictive Control (MPC) to decrease the computation cost. The neighborhood optimization is used to coordinate subsystem-based MPC to guarantee the global performance. Meanwhile, the optimization target of each subsystem-based MPC is recalculated in each control period according to the current starting temperature. The experimental results show the efficiency of the proposed method.