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

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.

Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications

“…This book contains the successful invited submissions [1][2][3][4][5][6][7][8][9][10][11][12][13] to a Special Issue of Mathematics on the subject area of "Bioinspired Intelligent Algorithms for Optimization, Modeling and Control: Theory and Applications".…”

“…The research results presented in [9] analyze stability for the control of an actuated electric vehicle with its respective validation. In [10], an optimal operation for reduced energy consumption in an air conditioning system based on a neural controller is considered. In this way, identification is a well-known methodology to obtain an accurate model of a complex dynamic system, and with the obtained model, it is possible to solve difficult nonlinear problems such as those represented by complex networks, as exemplified in [11] with a neural identifier used to design an impulsive pinning control.…”