Closed-Loop System Identification of Restricted Complexity Models Using Iterative Refinement

Abstract

A novel technique for identifying reduced-order models in the closed-loop is presented. The method arrives at a process model and its corresponding compensator in an iterative fashion by introducing a series of step chan at the manipulated variable. The bias introduced into the identification data set by the closed-loop system, coupled with a control-relevant prefilter, yields a model whose corresponding control system improves its performance at every step. The method is appealing to chemical engineering practitioners because it combines the tasks of system identification with controller commissioning to produce a simple-to-use yet reliable autotuning procedure.