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Disturbance Rejection using Neuro-Fuzzy Controllers: Special emphasis on HVAC Application - Couverture souple

KADRI, MUHAMMAD BILAL

 
9783639210415: Disturbance Rejection using Neuro-Fuzzy Controllers: Special emphasis on HVAC Application
Couverture souple
ISBN 10 : 3639210417 ISBN 13 : 9783639210415
Editeur : VDM Verlag, 2009

Synopsis

Non-linear uncertain systems are difficult to model and control especially when the system has significant amount of disturbances. Achieving tight control performance in the face of poorly measured disturbance is a difficult objective. To reject the disturbances different methodologies have been proposed. The advantages and disadvantages of Internal model Control (IMC) are discussed. Hybrid control schemes has been proposed which incorporate the feedforward of the measured disturbance along with IMC. The benefits of Fuzzy Relational Model in the representation of the measurement uncertainty constitute a major portion of the book. A novel approach to defuzzification is explored which can significantly reduce the control activity. A novel two-stage approach is proposed which is able to provide a better representation of the measurement uncertainty in the fuzzy control signal and consequently less control activity. Artificial Neural Network model of the laboratory air handling unit has been developed. Results from the simulation as well as from the real system are included.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Non-linear uncertain systems are difficult to model and control especially when the system has significant amount of disturbances. Achieving tight control performance in the face of poorly measured disturbance is a difficult objective. To reject the disturbances different methodologies have been proposed. The advantages and disadvantages of Internal model Control (IMC) are discussed. Hybrid control schemes has been proposed which incorporate the feedforward of the measured disturbance along with IMC. The benefits of Fuzzy Relational Model in the representation of the measurement uncertainty constitute a major portion of the book. A novel approach to defuzzification is explored which can significantly reduce the control activity. A novel two-stage approach is proposed which is able to provide a better representation of the measurement uncertainty in the fuzzy control signal and consequently less control activity. Artificial Neural Network model of the laboratory air handling unit has been developed. Results from the simulation as well as from the real system are included.

Biographie de l'auteur

BS Computer System Engineering,GIKI,Pakistan,1998-2002, DPhil in Engineering Science, Research Area: Neuro-Fuzzy Controls, University of Oxford, UK, 2005-2009, Presently working as an Assistant Professor at NUST, Pakistan

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag
Date d'édition
2009
Langue
anglais
ISBN 10 
3639210417
ISBN 13 
9783639210415
Reliure
Broché
Nombre de pages
204
Coordonnées du fabricant
non disponible
Personne responsable
non disponible