Nonlinear System Identification of a Refrigeration System

Abstract

Applications of advanced control algorithms are important in the refrigeration field to achieve low-energy costs and accurate set-point tracking. However, the designing and tuning of control systems depend on dynamic mathematical models. Approaches like analytical modeling can be time-consuming because they usually lead to a large number of differential equations with unknown parameters. In this work, the application of system identification with the fast recursive orthogonal least square (FROLS) algorithm is proposed as an alternative to analytical modeling to develop a process dynamic model. The evaporating temperature (EVT), condensing temperature (CDT) and useful superheat (USH) are the outputs of interest for this system; covariance analysis of the candidate inputs shows that the model should be single-input–single-output (SISO). Good simulation results are obtained with two different validation data, with average output errors of 0.0343 (EVT model), 0.0079 (CDT model) and 0.1578 (USH model) for one of the datasets, showing that this algorithm is a valid alternative for modeling refrigeration systems.