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.
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.
In the present work, parameters for adapting the behavior of the uniaxial three-element viscoelastic constitutive model with integer and fractional index derivatives to the mechanical evolution of an epoxy-composite material reinforced with long random henequen fibers, were determined. Cyclic loading–unloading with 0.1%, 0.2%, 0.3%, …, 1.0% controlled strain and staggered fluency experiments at 5 MPa, 10 MPa, and 15 MPa constant tension were performed in stages, and the obtained data were used to determine and validate the model’s parameter values. The Inverse Method of Identification was used to calculate the parameters, and the Particle Swarm Optimization (PSO) method was employed to achieve minimization of the error function. A comparison between the simulated uniaxial results and the experimental data is demonstrated graphically. There exists a strong dependence between properties of the composite and the fiber content (0 wt%, 9 wt%, 14 wt%, 22 wt%, and 28 wt% weight percentage fiber/matrix), and therefore also of the model parameter values. Both uniaxial models follow the viscoelastic behavior of the material and the fractional index version presents the best accuracy. The latter method was noted to be adequate for determination of the aforementioned constants using non-large experimental data and procedures that are easy to implement.
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