CR-IDENT: A MATLAB TOOLBOX FOR MULTIVARIABLE CONTROL-RELEVANT SYSTEM IDENTIFICATION

Abstract

This paper describes CR-IDENT, a Matlab-based toolbox that implements a comprehensive procedure for multivariable control-relevant system identification aimed primarily at process system applications. The toolbox consists of modules for multivariable input signal design (multisine and PRBS), frequency response estimation, and control-relevant frequency response curvefitting, leading to models whose end use is the design of high-performance control systems. An important component in the implementation of this design procedure is its reliance on a priori knowledge of the system of interest to design input signals meeting both theoretical and practical user requirements. Data from identification testing using these signals is the basis for the subsequent steps of frequency-response estimation and control-relevant parameter estimation, with the final result being a discrete-time state-space model that serves as the nominal model for Model Predictive Control. A high-purity distillation column example is presented to illustrate the benefits of the toolbox, from experiment design to closed-loop control.