An integrated methodology for plant-friendly input signal design and control-relevant estimation of highly interactive processes

A novel methodology that integrates a directional multisine input design procedure and control-relevant parameter estimation is developed in this paper, leading to desirable models for the control of highly interactive multivariable process systems. A priori information on system directionality is utilized in the input design procedure and recognized in the subsequent parameter estimation step, which consists of control-relevant curvefitting of frequency responses obtained from identification data. With input-output data based on a directionally adjusted input signal, the control-relevant parameter can accurately estimate the dynamic singular values of a system. As a result, a systematic procedure for generating a control-relevant model with balanced gain directionality is developed, appropriate for highly interactive processes. A case study involving a binary distillation column and Model Predictive Control is presented in this paper to demonstrate the effectiveness of the proposed approach.