Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system

Flavio Muñoz, Edgar N. Sanchez, Yudong Xia, Shiming Deng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)196-206
Number of pages11
JournalInternational Journal of Refrigeration
Publication statusPublished - 1 Jul 2017


  • Direct expansion air conditioning system
  • Inverse optimal control
  • Neural network
  • Real-time implementation

ASJC Scopus subject areas

  • Building and Construction
  • Mechanical Engineering

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