
Document IIF
Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system.
Régulation neuronale inverse optimale en temps réel de la température et l’humidité intérieures dans un système de conditionnement d’air (C/A) à détente directe.
Auteurs : MUNOZ F., SANCHEZ E. N., XIA Y., et al.
Type d'article : Article, Article de la RIF
Résumé
A real-time neural inverse optimal control for the simultaneous control of indoor air temperature and humidity using a direct expansion (DX) air conditioning (A/C) system has been developed and the development results are reported in this paper. A recurrent high order neural network (RHONN) was used to identify the plant model of an experimental DX A/C system. Based on this model, a discrete-time inverse optimal control strategy was developed and implemented to an experimental DX A/C system for simultaneously controlling indoor air temperature and humidity. The neural network learning was on-line performed by extended Kalman filtering (EKF). This control scheme was experimentally tested via implementation in real time using an experimental DX A/C system. The obtained results for trajectory tracking illustrated the effectiveness of the proposed control scheme.
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Pages : 196-206
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Détails
- Titre original : Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system.
- Identifiant de la fiche : 30022354
- Langues : Anglais
- Source : International Journal of Refrigeration - Revue Internationale du Froid - vol. 79
- Date d'édition : 07/2017
- DOI : http://dx.doi.org/10.1016/j.ijrefrig.2017.04.011
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