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Metrics for Interaction Assessment in Multivariable Control Systems Using Directional Analysis
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2018-01-16 00:00:00 , DOI: 10.1021/acs.iecr.7b03671
Abhinav Garg 1 , Arun K. Tangirala 1
Affiliation  

This work presents a novel method for assessing and quantifying the level of interactions in multivariable control systems in a hierarchical manner. The proposed metrics are based on the total directed (causal) power transfer between a pair of variables, constructed from a jointly linear stationary representation of the process. Three prime features of these metrics, specifically, (i) the ease of interpretation and versatility in accommodating different controller structures and operating conditions, (ii) the ability to use both first-principles linearized and data-driven (empirical) models, and (iii) the means for quantifying and evaluating the suitability of a decentralized versus a centralized control scheme, make them highly practical and valuable in the design and assessment of multivariable control schemes. An important outcome of this work is also an operator-friendly visual tool for inspection of interactions in various loops that can be generated for different tuning methods and controller configurations. Simulation studies of four different benchmark processes are presented to demonstrate the efficacy of the proposed method.

中文翻译:

基于方向分析的多变量控制系统交互评估指标

这项工作提出了一种新颖的方法,用于以分层方式评估和量化多变量控制系统中的交互作用水平。提议的度量基于一对变量之间的总有向(因果)功率传递,该变量是根据过程的联合线性固定表示构造的。这些度量标准的三个主要特征,特别是(i)适应不同控制器结构和操作条件的易于解释和多功能性;(ii)既可以使用第一原理线性化模型也可以使用数据驱动(经验)模型,并且( iii)量化和评估分散控制方案与集中控制方案的适用性的方法,使其在多变量控制方案的设计和评估中具有很高的实用性和价值。这项工作的重要成果还在于,它是一种操作员友好的可视化工具,用于检查可以为不同的调整方法和控制器配置生成的各种循环中的交互作用。提出了四种不同基准过程的仿真研究,以证明所提出方法的有效性。
更新日期:2018-01-16
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