This work describes a new photovoltaic (PV) sun tracker design methodology that utilizes the advantages that the orientation and efficiency of the PV panel offer due to the latitude of the installation zone. Furthermore, the proposed design methodology is validated experimentally via the implementation of a solar tracker with dual axes at a specific location (27.5° latitude). In this case, the methodology enables the incorporation of a high-availability, low-accuracy, and low-cost tracking mechanism. Based on the results, the feasibility of this type of solar tracker for latitudes close to 30° is demonstrated, as this tracking system costs 27% less than the traditional commercial systems that use slew drives. This system increases the collection efficiency by 24% with respect to a fixed device. The proposed methodology, which is based on an orientation efficiency chart, can be applied to the construction or control of other types of solar tracker systems.

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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.