Real-time neural inverse optimal control for indoor air temperature and humidity in a direct expansion (DX) air conditioning (A/C) system |
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| Affiliation: | 1. Instituto Tecnológico Superior de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregón, Sonora, Mexico;2. Centro de Investigación y de Estudios Avanzados del IPN, Av. del Bosque 1145, Zapopan, Jalisco, Mexico;3. Department of Building Services Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China;1. Department of Electrical Engineering, University of Chile, Av. Tupper 2007, Santiago, Chile;2. Advanced Mining Technology Center, University of Chile, Av. Tupper 2007, Santiago, Chile;3. Department of Electrical Engineering, CINVESTAV, Av. IPN 2508, México DF, México |
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| 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. |
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