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Economic Optimization and Control Based on Multi Priority Rank RTO and Double Layered MPC
Author(s) -
Pan HongGuang,
Zhong Weimin,
Wang ZaiYing
Publication year - 2018
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1730
Subject(s) - control theory (sociology) , model predictive control , rank (graph theory) , layer (electronics) , steady state (chemistry) , process (computing) , state (computer science) , set (abstract data type) , control (management) , computer science , mathematical optimization , control engineering , engineering , mathematics , algorithm , artificial intelligence , materials science , chemistry , combinatorics , programming language , operating system , composite material
Abstract A prevailing hierarchical structure in industrial process optimization and control includes three levels, i.e. , a real time optimization (RTO) level, a double layered model predictive control (MPC) level (which is composed of a steady‐state target calculation (SSTC) layer and a dynamic control layer), and a distributed control level. In this paper, a multi priority rank RTO algorithm, in which a new variable is introduced to uniformly express the set points, is presented to get optimal set points according to their importance levels. In order to guarantee the feasibility of the dynamic control layer during tracking the steady‐state targets calculated in SSTC layer, the region of attraction is added into the SSTC layer as additional constraints, hence, the steady‐state targets can be calculated online and transmitted to the dynamic control layer at each instant to guide the state to achieve the steady‐state state gradually. The effect of the above methods are illustrated through an example.