Control configuration selection for economic model predictive control

Abstract

Economic model predictive control (EMPC) is a control scheme that dictates a potentially dynamic operating policy to optimize the process economics. The objective function used in the EMPC may be a general nonlinear function that describes the process/system economics. Since this function is not derived on the basis of classical control considerations only (e.g., stabilization, tracking, and optimal control action calculation), selecting the appropriate control configuration and quantifying the influence of a given input on an economic cost is an important task for the proper design of an EMPC scheme. Owing to these considerations, we propose to utilize the relative degree of the economic cost function with respect to an input to identify and select inputs with the most influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and the presented control configuration selection for EMPC method is demonstrated using a chemical process example.