Tese: Ensemble Grey and Black-box System Identification for Friction Models
Aluno(a) : Walisson Chaves Ferreira PintoOrientador(a): Helon Ayala
Área de Concentração: Mecânica Aplicada
Data: 24/03/2021
Link para tese/dissertação: https://doi.org/10.17771/PUCRio.acad.53217
Resumo: The mathematical abstraction of a physical process is essential in engineering problems. There are many reasons for developing mathematical models of existing systems: i) for security and/or economic reasons, as it may be impractical or impossible to carry out experiments on the real system, ii) mathematical models are more flexible than physical prototypes, allowing a quick refinement of system designs to optimize various performance measures. The applications of the models can be divided into four parts, namely: design, estimation, control and monitoring. Some specific applications are i) simulations, ii) soft sensors, iii) performance evaluation, iv) statistical quality control and, v) fault detection and diagnosis. This work aims to: i) develop different classes of models capable of accurately simulating the output variable of a system, ii) evaluate the efficiency of optimization algorithms used in the parameter estimation task, iii) assess which friction model is the most appropriate to describe this phenomenon in a positioning system. The results show that combining models is an effective alternative to obtain more accurate simulation results. Nonlinear friction models were more adequate to describe this phenomenon, which had an asymmetric character, in the positioning system. In addition, the simulations performed with parameters estimated by the evolutionary algorithms showed better results. The decision tree-based optimizer, used in the second case study, proved to be equally effective compared to evolutionary algorithms.