Yuz Eissmann, Juan Ignacio

In this paper we consider the analysis of multiple-input multiple-output systems using the Participation Matrix, which provides a tool both for interaction measure and controller structure selection. For this matrix we present novel interpretations in the time and in the frequency domain, based on definition of Hilbert-Schmidt-Hankel norm. Moreover, the time domain interpretation is exploited to obtain an empirical estimate of the participation matrix directly from input-output data of the multivariable system.

A precursor to any advanced control solution is the step of obtaining an accurate model of the process. Suitable models can be obtained from phenomenological reasoning, analysis of plant data or a combination of both. Here, we will focus on the problem of estimating (or calibrating) models from plant data. A key goal is to achieve robust identification. By robust we mean that small errors in the hypotheses should lead to small errors in the estimated models. We argue that, in some circumstances, it is essential that special precautions be taken to ensure that robustness is preserved. We present several practical case studies to illustrate the results.