Parametric uncertainty modeling for robust control: a link to identification

The dynamic behaviour of a non-linear process can often be approximated wit h a time-varying linear model. In the presented methodology the dynamics is modeled non-conservatively as parametric uncertainty in linear lime invari ant models. The obtained uncertainty description makes it possible to perfo rm robustness analysis on a control system using the structured singular va lue. The idea behind the proposed method is to fit a rational function to t he parameter variation. The parameter variation can then be expressed as a linear fractional transformation (LFT), It is discussed how the proposed me thod can be utilized in identification of a nominal model with uncertainty description. The method is demonstrated on a binary distillation column ope rating in the LV configuration. The dynamics of the column is approximated by a second order linear model, wherein the parameters vary as the operatin g point changes. It is shown that a diagonal PI control structure provides robust performance towards variations in feed flow rate or feed concentrati ons. However including both liquid and vapor flow delays robust performance specifications cannot be satisfied with this simple diagonal control struc ture. (C) 1999 Elsevier Science Ltd. All rights reserved.