Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76241
Title: Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system
Authors: Munoz, F
Sanchez, EN
Xia, YD 
Deng, SM 
Keywords: Direct expansion air conditioning system
Inverse optimal control
Neural network
Real-time implementation
Issue Date: 2017
Publisher: Elsevier
Source: International journal of refrigeration, 2017, v. 79, p. 196-206 How to cite?
Journal: International journal of refrigeration 
Abstract: 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.
URI: http://hdl.handle.net/10397/76241
ISSN: 0140-7007
EISSN: 1879-2081
DOI: 10.1016/j.ijrefrig.2017.04.011
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