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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.

Author(s) : MUNOZ F., SANCHEZ E. N., XIA Y., et al.

Type of article: Article, IJR article

Summary

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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Details

  • Original title: Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system.
  • Record ID : 30022354
  • Languages: English
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 79
  • Publication date: 2017/07
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2017.04.011

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