Identificação de sistemas usando uma sequência PRBS com a inclusão da dinâmica da variável de entrada.

Abstract

This work intends to be a contribution to the field of system identification, which is one of the methods available for the construction of models and has been gradually becoming the method of choice among the different system modeling alternatives. The system identification method addressed in this study was developed using a signal known as a Rseudorandom Binary Sequence (PRBS). This class of excitation signal has been increasingly used due to its interesting excitation characteristics and fewer generation and application difficulties. One characteristic that has led to the acceptance of PRBS in different industries is the small degree of disturbance to plant operations, which is why it is commonly called a "friendly signal". However, the currently proposed method for PRBS signals applied to industrial processes does not consider the dynamics of the input variable. The present work offers an alternative methodology for the projection of this type of signal that includes the influence of the input variable in the modeling process using the system identification method. Two case studies were carried out in order to assess and compare the proposed methodology with the currently available methodology for the projection of this type of excitation signal. In the first, the influence of the final control element in the identified model was assessed, revealing that the signal applied using the proposed methodology was able to generate data that lead to a more representative model of the system under study. In the second case study, the influence of the input variable was simulated, revealing that the proposed methodology is more adequate for exciting the system in the conditions under which the system was simulated. The results demonstrate that the novel methodology proposed for the projection of PRBS signals, which considers the dynamics of the input variable, has a predictive capacity for generating a better process model that the methodology currently adopted in the literature