Relative gain array analysis for uncertain process models

Abstract

Abstract Relative gain array (RGA) analysis has been widely used in process control to identify promising control structures and to characterize the degree of process interactions between controlled and manipulated variables. However, the influence of process model uncertainty on RGA analysis has received little attention. Analytical expressions for RGA uncertainty bounds are derived for 2 × 2 control problems and for general, n × n control problems. Both worst‐case bounds and statistical uncertainty bounds are derived. Several simulation examples illustrate the new results. The RGA uncertainty bounds provide useful information concerning model accuracy requirements and the robustness of decentralized control systems.