Experimental application of a neural constrained model predictive controller based on reference system
Título del documento: Experimental application of a neural constrained model predictive controller based on reference system
Revista: Latin American applied research
Base de datos: PERIÓDICA
Número de sistema: 000332255
ISSN: 0327-0793
Autores: 1
1
1
Instituciones: 1Universidade Federal de Uberlandia, Faculdade de Engenharia Quimica, Uberlandia, Minas Gerais. Brasil
Año:
Periodo: Ene
Volumen: 58
Número: 1
Paginación: 51-62
País: Argentina
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés The proposed constrained model predictive control (MPC) is based on a successive linearization of a neural model at each sampling time and the closed loop response is subject to a first order reference system as set of equality constraints. In addition the system inputs are subject to hard constraints. In order to satisfy both types of constraints simultaneously it was needed to include a slack vector in the equality constraints. This slack vector provides more flexibility in the control moves in order to render the solution of the optimization problem feasible. The proposed MPC was implemented in an experimental pH neutralization plant. Results showed a very satisfactory performance of the proposed strategy
Disciplinas: Ingeniería,
Química
Palabras clave: Ingeniería industrial,
Ingeniería química,
Sistemas de control en tiempo real,
Redes neuronales,
Control de procesos,
pH,
Control basado en modelos
Keyword: Engineering,
Chemistry,
Chemical engineering,
Industrial engineering,
Real-time control systems,
Neural networks,
Process control,
pH,
Model based control
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