Avaliação de desempenho de controladores preditivos multivariáveis
In advanced process control, the Model Predictive Control (MPC) may be considered the most important innovation in recent years and the standard tool for industrial applications due to the fact that it keeps the plant operating in the constraints more profitable. However, like every control algorith...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2013 |
| País: | Brasil |
| Institución: | Universidade Federal de Sergipe (UFS) |
| Repositorio: | Repositório Institucional da UFS |
| Idioma: | portugués |
| OAI Identifier: | oai:oai:ri.ufs.br:repo_01:riufs/5044 |
| Acceso en línea: | https://ri.ufs.br/handle/riufs/5044 |
| Access Level: | acceso abierto |
| Palabra clave: | Engenharia elétrica Controladores elétricos Controladores PID Sistemas lineares de controle Teoria do controle Controle H2 Controlador preditivo Avaliação de desempenho de controladores Controle preditivo multivariável LQG benchmark modificado IHMPC benchmark Índice de desempenho Control theory Electric controllers Electric engineering H2 Control Linear control systems PID controllers Predictive control Performance evaluation of controllers Multivariable predictive control LQG benchmark modified Performance index CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
| Sumario: | In advanced process control, the Model Predictive Control (MPC) may be considered the most important innovation in recent years and the standard tool for industrial applications due to the fact that it keeps the plant operating in the constraints more profitable. However, like every control algorithm, the MPC after some time in operation rarely works as originally designed. Thus, to preserve the benefits of MPC systems for a long period of time, their performance needs to be monitored and evaluated during the operation. This task require the presence of reliable and effective tools to detect when the controller performance is below of the desirable, to define the need, or not, of recommissioning the system. Thus, the objective of this work is development of techniques for monitoring and evaluating the performance of multivariable predictive controllers, being developed two new tools: LQG benchmark Modified and IHMC benchmark. The results obtained from numerical simulations were satisfactory and consistent with the technical literature applied in the developments of the evaluators, which were used in the monitoring of the control system MPC of the oil-water-gas three-phase separation process, offering an appropriate solution and providing subsidies for implementations in real industrial systems. |
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