RGA analysis of dynamic process models under uncertainty

Abstract

The aim of this paper is to gain insights into how process dynamics can affect control configuration decision based on relative gain array (RGA) analysis in the face of model uncertainty. Analytical expressions for worst-case bounds of uncertainty in steady-state and dynamic RGA are derived for two inputs two outputs (TITO) plant models. A simulation example which has been used in several prior studies is considered here to demonstrate the results. The obtained bounds of uncertainty in RGA provide valuable information pertaining to the necessity of robustness and accuracy in the model of decentralized multivariable systems.