PROPOSTA DE METODOLOGIA RECURSIVA-ITERATIVA PARA IDENTIFICAÇÃO FUZZY DE SISTEMAS NÃO LINEARES ESTOCÁSTICOS EM MALHA FECHADA

Abstract

Most methods of identifcation of closed-loop dynamic systems are developed for linear and deterministic systems. However, most closed loop systems are nonlinear dynamic systems. In addition, such systems are subject to stochastic perturbations. Considering this problem, this work presents a methodology for the identifcation of closed loop stochastic nonlinear systems. For this purpose, the proposed methodology uses a local approach to identify nonlinear dynamic systems, that is, a set of Box-Jenkins local models are used to identify the dynamics of the nonlinear system. In this work, the nonlinear system is modeled through a Takagi-Sugeno fuzzy inference system, where the parameters of the antecedent of the fuzzy rules are estimated with the fuzzy clustering algorithm GustafsonKessel and the consequent Box-Jenkins model parameters are estimated with the fuzzy fuzzy RIV (Refned Instrumental Variable) and fuzzy IVARMA (Instrumental Variable ARMA) algorithms. The proposed method is applied in the identifcation of a closed-loop nonlinear thermal plant.