Optimization based flight control design tools depend on automatic linearization tools, such as Simulink®’s LINMOD, to extract linear models. In order to ensure the usefulness and correctness of the generated linear model, this linearization must be accurate. So a method of independently verifying the linearized model is needed. This thesis covers the automation of a system identification tool, CIFER®, for use as a verification tool integrated with CONDUIT®, an optimization based design tool. Several test cases are built up to demonstrate the accuracy of the verification tool with respect to analytical results and matches with LINMOD. Several common nonlinearities are tested, comparing the results from CIFER and LINMOD, as well as analytical results where possible. The CIFER results show excellent agreement with analytical results. LINMOD treated most nonlinearity as a unit gain, but some nonlinearities linearized to a zero, causing the linearized model to omit that path. Although these effects are documented within Simulink, their presence may be missed by a user. The verification tool is successful in identifying these problems when present. A section is dedicated to the diagnosis of linearization errors, suggesting solutions where possible.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-17T12:44:32Z No. of bitstreams: 1 Hugo Alves Velozo.pdf: 5196080 bytes, checksum: 14e9edcc07c0256cf726b1d0f7eb9a02 (MD5) / Made available in DSpace on 2017-04-17T12:44:32Z (GMT). No. of bitstreams: 1 Hugo Alves Velozo.pdf: 5196080 bytes, checksum: 14e9edcc07c0256cf726b1d0f7eb9a02 (MD5) Previous issue date: 2017-02-20 / CAPES / 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. / A maioria dos métodos de identifcação de sistemas dinâmicos em malha fechada são desenvolvidos para sistemas lineares e determinísticos. Entretanto, a maioria dos sistemas operando em malha fechada são sistemas dinâmicos não lineares. Além disso, esses sistemas estão sujeitos a perturbações de natureza estocástica. Considerando essa problemática, este trabalho apresenta uma metodologia para identifcação de sistemas não lineares estocásticos em malha fechada. Para isso, a metodologia proposta utiliza uma abordagem local de identifcação de sistemas dinâmicos não lineares, ou seja, um conjunto de modelos locais Box-Jenkins são utilizados para identifcar a dinâmica do sistema não linear. Neste trabalho, o sistema não linear é modelado por meio de um sistema de inferência fuzzy Takagi-Sugeno, onde os parâmetros do antecedente das regras fuzzy são estimados com o algoritmo de agrupamento fuzzy Gustafson-Kessel e o parâmetros do modelo Box-Jenkins do consequente são estimados com os algoritmos RIV (Refned Instrumental Variable) fuzzy e IVARMA (Instrumental Variable ARMA) fuzzy. O método proposto é aplicado na identifcação de uma planta térmica não linear em malha fechada.