Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-09-04T18:35:15Z No. of bitstreams: 1 OrlandoRochaFilho.pdf: 10104010 bytes, checksum: 7c750a6e03597fc2e7f7474b62c35a46 (MD5) / Made available in DSpace on 2017-09-04T18:35:15Z (GMT). No. of bitstreams: 1 OrlandoRochaFilho.pdf: 10104010 bytes, checksum: 7c750a6e03597fc2e7f7474b62c35a46 (MD5) Previous issue date: 2017-04-24 / This thesis presents a maximum likelihood based modeling approach applied to dynamic systems operating in non-stationary environment that uses recursive parametric estimation based on the method of fuzzy instrumental variable. The context is evolving and the idea is to guarantee a robust for estimation of the parameters of noise-corrupted experimental data. The methodology consists of an evolving fuzzy clustering algorithm based on the similarity of the data which employs an adaptive distance norm based on the maximum likelihood criterion that use an adaptive search strategy on the experiment in order to avoid the curse of dimensionality related to the number of rules created during data clustering of the data set. The computational and experimental results to exemplify the proposed methodology are: statistical analysis of the fuzzy instrumental variable inserted in the evolving context; black box modeling of a thermal plant; identification of a benchmark nonlinear system widely published in the literature and the black box modeling of a 2DOF helicopter. These examples are used to illustrate the performance and efficiency by operating in a non–stationary environment. / Nesta tese é apresentada uma proposta de modelagem baseada máxima verossimilhança aplicada a sistemas dinâmicos operando em ambiente não-estacionário que utiliza a estima- ção paramétrica recursiva baseada no método de variável instrumental nebulosa, inserido no contexto evolutivo, no sentido de garantir robustez para estimação dos parâmetros diante de dados experimentais corrompidos por ruído. A metodologia é composta por um algoritmo de agrupamento nebuloso evolutivo baseado na similaridade dos dados que emprega uma norma de distância adaptativa baseada no critério de máxima verossimilhança que utiliza uma estratégia de busca adaptativa no experimento para evitar o problema da maldição de dimensionalidade relacionada ao número de regras criadas durante o agrupamento do conjunto de dados. Os resultados computacionais e experimentais para exemplificação da metodologia proposta são: análise estatística da variável instrumental nebulosa inserida no contexto evolutivo; na modelagem caixa preta de uma planta térmica (processo térmico); identificação de um sistema não-linear amplamente divulgado na literatura e a modelagem caixa preta de um helicóptero com dois graus de liberdade que ilustra o desempenho e a eficiência operando ambiente não-estacionário.

<p>The complexity of industrial systems and the mathematical models to describe them increases. In many cases, point sensors are no longer sufficient to provide controllers and monitoring instruments with the information necessary for operation. The need for other types of information, such as audio and video, has grown. These are examples of information-rich signals for which suitable applications range in a broad spectrum from micro-electromechanical systems and bio-medical engineering to paper making and steel production.</p><p>Recursive parameter estimation algorithms are employed to identify parameters in a mathematical model from measurements of input and output signals. For accurate parameter estimation, the input signal must be <i>persistently exciting, i.e.</i> such that important features of the modelled system are reflected in the output over a sufficient period of time.</p><p>The Stenlund-Gustafsson (SG) algorithm, a Kalman filter based method for recursive parameter estimation in linear regression models, that does not diverge under lack of excitation, is studied. The stationary properties of the algorithm and the corresponding Riccati equation are formulated in terms of the excitation space spanned by the regressor vectors.</p><p>Furthermore, it is shown that the Riccati equation of the studied algorithm can be solved element-wise. Convergence estimates for the elements of the solution to the Riccati equation are provided, directly relating convergence rate to the signal-to-noise ratio in the regression model. An element-wise form of the parameter update equation is also given, where the connection to specific elements of the solution to the Riccati equation is apparent.</p><p>The SG-algorithm is employed for two applications with audio signals. One is in an acoustic echo cancellation setting where its performance is shown to match that of other well-known estimation techniques, such as the normalized least mean squares and the Kalman filter. When the input is not sufficiently exciting, the studied method performs best of all considered schemes.</p><p>The other application is the Linz-Donawitz (LD) steel converter. The converter consists of a vessel with molten metal and foam is produced to facilitate chemical reactions. A common problem, usually referred to as slopping, arises when the foam rises above the limits of the vessel and overflows. A warning system is designed, based on the SG-algorithm and change detection methods, to give alarms before slopping occurs. A black-box model relates different sensor values of which one is the microphone signal picked up in the area above the converter. The system detected slopping correctly in 80% of the blows in field studies at SSAB Oxelösund.</p><p>A practical example of a vision-based system is provided by cavity form estimation in a water model of the steel bath. The water model is captured from the side by a video camera. The images together with a non-linear model are used to estimate important process parameters, related to the heat and mass transport in the LD-converter.</p>

The complexity of industrial systems and the mathematical models to describe them increases. In many cases, point sensors are no longer sufficient to provide controllers and monitoring instruments with the information necessary for operation. The need for other types of information, such as audio and video, has grown. These are examples of information-rich signals for which suitable applications range in a broad spectrum from micro-electromechanical systems and bio-medical engineering to paper making and steel production. Recursive parameter estimation algorithms are employed to identify parameters in a mathematical model from measurements of input and output signals. For accurate parameter estimation, the input signal must be persistently exciting, i.e. such that important features of the modelled system are reflected in the output over a sufficient period of time. The Stenlund-Gustafsson (SG) algorithm, a Kalman filter based method for recursive parameter estimation in linear regression models, that does not diverge under lack of excitation, is studied. The stationary properties of the algorithm and the corresponding Riccati equation are formulated in terms of the excitation space spanned by the regressor vectors. Furthermore, it is shown that the Riccati equation of the studied algorithm can be solved element-wise. Convergence estimates for the elements of the solution to the Riccati equation are provided, directly relating convergence rate to the signal-to-noise ratio in the regression model. An element-wise form of the parameter update equation is also given, where the connection to specific elements of the solution to the Riccati equation is apparent. The SG-algorithm is employed for two applications with audio signals. One is in an acoustic echo cancellation setting where its performance is shown to match that of other well-known estimation techniques, such as the normalized least mean squares and the Kalman filter. When the input is not sufficiently exciting, the studied method performs best of all considered schemes. The other application is the Linz-Donawitz (LD) steel converter. The converter consists of a vessel with molten metal and foam is produced to facilitate chemical reactions. A common problem, usually referred to as slopping, arises when the foam rises above the limits of the vessel and overflows. A warning system is designed, based on the SG-algorithm and change detection methods, to give alarms before slopping occurs. A black-box model relates different sensor values of which one is the microphone signal picked up in the area above the converter. The system detected slopping correctly in 80% of the blows in field studies at SSAB Oxelösund. A practical example of a vision-based system is provided by cavity form estimation in a water model of the steel bath. The water model is captured from the side by a video camera. The images together with a non-linear model are used to estimate important process parameters, related to the heat and mass transport in the LD-converter.