A method to coordination for decentralized multi- MPC controllers in integrated plant

In this paper, a method to coordinate multi-decentralized model predictive controllers(DMMPC), which is used as a strategy to control the integrated plant through decomposing the total system model into different functional sub-models, is proposed. Usually, an integrated plant is also called a large scale systems. The main aim is to relieve the expensive computation load and for improving the flexibility and reliability for operation. How to control and coordinate the possible dynamic coupling and constraints among subsystems has already become an important problem. In order to accomplish the overall objective, coordinating the relationship between optimizer located in upper layer and multi-decentralized MPC controllers located in down layer is a challenging work. A stochastic probability density functions(PDFs) coordination rule is applied to coordinate optimizer with decentralized MPC controllers in the hierarchical control structure. This approach provides the guarantee that local optimal solutions for dominant controlled variables approximate their global optimal solutions under considering uncertainties and interactions among subunits on closed-loop stability condition. As a study case, an integrated plant which consists of two subunits connected via other process and some intermediate tanks under severe constraints while considering maximization economic performance is used to demonstrate feasibility and efficiency of this framework.