Economic Model Predictive Control of Enhanced Operation Performance for Industrial Hierarchical Systems

Abstract

Economic operation performance has been a primary consideration in recent control strategy designs for industrial processes. This performance refers to the accumulated economic cost corresponding to dynamic evolution and final steady-state phase. Incorporating an economic model predictive controller (EMPC) to the industrial hierarchy, i.e., the real-time optimizer followed by the advanced controller EMPC, is a well-reasoned approach for performance improvement. However, model mismatch among layers may lead to infeasibility issue and final static operation error/offset. This article presents a novel offset-free EMPC scheme, which admits algorithm feasibility in the presence of process constraints and model mismatch. The convergence and offset-free properties w.r.t. the best attainable steady state are guaranteed. Specifically, this scheme contains a dynamic target optimization stage and an EMPC stage. The stabilization formulations are designed, respectively, for dissipative and nondissipative systems, to maximum possible performance improvement. Finally, the method is illustrated by the typical chemical plant examples. The results show that the offset-free EMPC scheme aligns well in the hierarchically controlled system with emphasis on performance improvement.