Detalhes bibliográficos
| Ano de defesa: |
2017 |
| Autor(a) principal: |
Cruz, Diego Déda Gonçalves Brito
 |
| Orientador(a): |
Sotomayor, Oscar Alberto Zanabria |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de Sergipe
|
| Programa de Pós-Graduação: |
Pós-Graduação em Engenharia Elétrica
|
| Departamento: |
Não Informado pela instituição
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://ri.ufs.br/handle/riufs/5028
|
Resumo: |
Model predictive control (MPC) strategies have become the standard for advanced control applications in the process industry. Significant benefits are generated from the MPC's capacity to ensure that the plant operates within its constraints more profitably. However, like any controller, after some time under operation, MPCs rarely function as when they were initially designed. A large percentage of performance degradation of MPC is associated with the deterioration of model that controller uses to predict process outputs and calculate inputs. The objective of the present work is implementation of mathematical methods that can be used to detect model-plant mismatch in linear and nonlinear MPC systems. In this work, techniques based on cross correlation, partial correlation and mutual information are implemented and tested by numerical simulation in case studies characteristic of the petrochemical industry, represented by linear and nonlinear models, operating under MPC control. The results obtained through the applying the techniques are analyzed and compared as to their efficiency is not intended to offer their potential for real industrial applications. |
