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.
Highlights
Not just a comfortable level of indoor air temperature is the only objective of air conditioning (A/C) systems [1,2]
A neural network, even with a single hidden layer, has the ability to uniformly approximate any continuous function over a compact domain, considering that there are enough synaptic connections in the NN, this fact has been demonstrated in different theoretical works
The identification and control scheme are applied to the dynamic model for the variable speed (VS) direct expansion (DX) A / C system
Summary
Not just a comfortable level of indoor air temperature is the only objective of air conditioning (A/C) systems [1,2]. For humidity and temperature control in air VS DX A/C, different control strategies have been designed and used, from the traditional proportional integral derivative (PID) control to advanced and robust controllers [10] These include direct digital control [11], multi-input multi-output control method [12], neural network based [13,14,15], fuzzy logic controller (FLC) [16], Genetic and Swarm Algorithms [17] and Adaptive Control [18]. The determination a posteriori of the cost functional for the stabilizing feedback control law is an essential feature of the inverse approach [24,32] Applications of this complete control scheme are illustrated in: [33], where an optimal inverse neural control for discrete-time impulsive systems is determined.
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