Ponencia
Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger
Autor/es | Rodríguez Ramírez, Daniel
Ruiz Arahal, Manuel Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2001 |
Fecha de depósito | 2020-04-22 |
Publicado en |
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ISBN/ISSN | 1474-6670 |
Resumen | Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical ... Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this paper the use of a Neural Network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this later problem. Simulation and experimental results are given using a heat exchanger. |
Cita | Rodríguez Ramírez, D., Arahal, M. R. y Camacho, E.F. (2001). Neural Network Based Min-Max Predictive Control. Application to a Heat Exchanger. En IFAC Adaptation and Learning in Control and Signal Processing (115-120), Cemobbio-Como. Italy: Elsevier. |
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