A comparison study of MPC strategies based on minimum variance control index performance
Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D,...
- Autores:
-
BORRERO-SALAZAR, Alex A.
CARDENAS-CABRERA, Jorge M.
BARROS-GUTIERREZ, Daniel A.
JIMÉNEZ-CABAS, Javier A.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5002
- Acceso en línea:
- https://hdl.handle.net/11323/5002
https://repositorio.cuc.edu.co/
- Palabra clave:
- MPC design
Minimum variance control
FCOR
CSTR
Diseño MPC
Control de Mínima Varianza
- Rights
- openAccess
- License
- CC0 1.0 Universal
| Summary: | Model Predictive Control (MPC) is a useful tool when controlling processes that handle a large number of input and output variables. This study presents a comparison of different MPC strategies when they are subjected to control process variables directly. The strategies studied are IMC, GPC, MPC-D, MPC-DR, and DMC. Evaluation of the performance of the controlled loop was performed with the filtering and correlation analysis algorithm (FCOR). The methodology proposed is validated in a Continuous Stirred-Tank Reactor (CSTR) case study. Discrete predictive control demonstrated the best results in this study. |
|---|