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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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Format PDF

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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