Global Self-optimizing Control Measurement Combination Selection Based on Constraint Processing

CHEN Zhongfa, WANG Yan, JI Zhicheng

Control Engineering of China ›› 2025, Vol. 32 ›› Issue (5) : 913-920.

Control Engineering of China ›› 2025, Vol. 32 ›› Issue (5) : 913-920.

Global Self-optimizing Control Measurement Combination Selection Based on Constraint Processing

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Abstract

For the steady-state operation problem of continuous industrial processes, a method of selecting the optimal measurement combination of global self-optimizing control based on constraint processing is proposed. The relationship between constraint and disturbance is deduced by establishing a linearized model of constraint. In order to select a subset of measurement variables, a penalty term is added to the global self-optimizing control optimization problem, and the optimal balance between the steady-state loss and the number of measurement variables was achieved by sparse column elimination of measurement variables that had little influence on the system control. On this basis, the interior point method of the penalty function is introduced to deal with the change of active constraints. Because the interference usually causes the change of active constraints in the actual industrial process, it is more realistic to consider the change of active constraints in the selection of global self-optimizing control measurement combination. The simulation results of the evaporation process verify the effectiveness of the proposed method in constraint treatment and measurement combination selection.

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Steady state / constraint processing / global self-optimizing control / measurement combination / column sparsity / interior point method

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CHEN Zhongfa, WANG Yan, JI Zhicheng. Global Self-optimizing Control Measurement Combination Selection Based on Constraint Processing[J]. Control Engineering of China, 2025, 32(5): 913-920

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