Abstract

The existing uncertainties present during the operation of dynamic processes could affect the performance of modeling systems, control strategies, and fault detection systems. Because of that, the study of uncertainty quantification and the design of prediction intervals has recently gained interest in representing the effect of uncertainty on future process behavior. However, these tools have been designed to focus on applying multiple-input single-output (MISO) systems. For this reason, this work aims to design a novel multiple-variable uncertainty region that results from the adaptation of prediction interval methods to a multiple-input multiple- output (MIMO) framework. To achieve this goal, the algorithm integrates the information provided by the prediction intervals identified for each separate output variable and the covariance matrix of the past error vectors to determine the ellipsoid region that will contain the future values of the modeled MIMO system. How this proposal handles the uncertainty effect of these multiple output variables is tested over simulated data from a coupled H & eacute;non map and real data from a heating, ventilation, and air conditioning system. The simulation results achieved in this work show the proposal's effectiveness in characterizing the uncertainty of multiple variables simultaneously when compared to applying the direct combination of multiple MISO prediction intervals.