In this manuscript, distinct approaches were used in order to obtain the best electrical power estimation from photovoltaic systems located at different selected places in Mexico. Multiple Linear Regression (MLR) and Gradient Descent Optimization (GDO) were applied as statistical methods and they were compared against an Adaptive Neuro-Fuzzy Inference System (ANFIS) as an intelligent technique. The data gathered involved solar radiation, outside temperature, wind speed, daylight hour and photovoltaic power; collected from on-site real-time measurements at Mexico City and Hermosillo City, Sonora State. According to our results, all three methods achieved satisfactory performances, since low values were obtained for the convergence error. The GDO improved the MLR results, minimizing the overall error percentage value from 7.2% to 6.9% for Sonora and from 2.0% to 1.9% for Mexico City; nonetheless, ANFIS overcomes both statistical methods, achieving a 5.8% error percentage value for Sonora and 1.6% for Mexico City. The results demonstrated an improvement by applying intelligent systems against statistical techniques achieving a lesser mean average error.
In this paper a model-free continuous nonlinear control law for the attitude of a quadrotor, based on the Attractive Ellipsoid Method and a saturation term, is proposed. This control law allows the vehicle to track aggressive maneuvers, such as multiple flips about the y axis of the body frame, with high angular velocities. The controller is designed through a singularity-free attitude representation based on a unit quaternion and its gains are computed by solving an optimization problem with LMIs. The proposed controller preserves the advantageous characteristics of the Attractive Ellipsoid Method and increases its robustness properties with the fast response of the nonlinear saturation term, minimizing as much as possible the attitude tracking error and assuring its convergence to a small neighborhood around the origin. A numerical study based on simulations is presented to analyze the advantages of the proposed approach, and experiments are presented to show the performance of the closed-loop system for tracking aggressive multiple flips, even in outdoors.
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