Optimal Operation for Reduced Energy Consumption of an Air Conditioning System Using Neural Inverse Optimal Control

Flavio Muñoz, Ramon Garcia-Hernandez, Jose Ruelas, Juan E. Palomares-Ruiz and Carlos Álvarez-Macías
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Flavio Muñoz: Tecnologico Nacional de Mexico/ITS de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregon 85024, Sonora, Mexico
Ramon Garcia-Hernandez: Tecnologico Nacional de Mexico/Instituto Tecnologico de La Laguna, Blvd. Revolucion y Av. Instituto Tecnologico de La Laguna S/N, Col. Centro, Torreon 27000, Coahuila, Mexico
Jose Ruelas: Tecnologico Nacional de Mexico/ITS de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregon 85024, Sonora, Mexico
Juan E. Palomares-Ruiz: Tecnologico Nacional de Mexico/ITS de Cajeme, Carretera Internacional a Nogales km 2, Cd. Obregon 85024, Sonora, Mexico
Carlos Álvarez-Macías: Tecnologico Nacional de Mexico/Instituto Tecnologico de La Laguna, Blvd. Revolucion y Av. Instituto Tecnologico de La Laguna S/N, Col. Centro, Torreon 27000, Coahuila, Mexico

Mathematics, 2022, vol. 10, issue 5, 1-15

Abstract: For a comfortable thermal environment, the main parameters are indoor air humidity and temperature. These parameters are strongly coupled, causing the need to search for multivariable control alternatives that allow efficient results. Therefore, in order to control both the indoor air humidity and temperature for direct expansion (DX) air conditioning (A/C) systems, different controllers have been designed. In this paper, a discrete-time neural inverse optimal control scheme for trajectories tracking and reduced energy consumption of a DX A/C system is presented. The dynamic model of the plant is approximated by a recurrent high-order neural network (RHONN) identifier. Using this model, a discrete-time neural inverse optimal controller is designed. Unscented Kalman filter (UKF) is used online for the neural network learning. Via simulation the scheme is tested. The proposed approach effectiveness is illustrated with the obtained results and the control proposal performance against disturbances is validated.

Keywords: direct expansion; air conditioning system; neural network; unscented Kalman filter; variable speed (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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