The sliding mode based differential flatness control is used to stabilize the error dynamics in view of unmodeled dynamics employing position, velocity and acceleration as reference values but feeding back to system only the position and velocity measurements. This controller is able to plan trajectories of control gains within the proposed scheme of the controller. By above this paper describes a sliding mode based differential flatness control to a leg-wheel hybrid robot, in order to design a robotic prototype with the ability to move an uneven ground. To prove the controller working a simulation in Matlab-Simulink using Simmechanics is made. The result of this work shows a controller that is able to follow the reference trajectories without overshoots and small chattering.
The work is devoted to solve allocation task problem in the distributed energy way in multi agents systems with multi-objective genetic algorithms. The paper shows the main advantages of genetic algorithms and the way to apply a new genetic operator using the solution information of the other agents for saving energy in the search of expand the solution of the optimal distribution.
This work seeks to analyze the feasibility to integrate a renewable hybrid energy resource in the DC smart grid for the campus of Universidad Militar Nueva Granada (UMNG), which is located in Cajica - Cundinamarca. Taking the wind energy as the selected renewable resource, we developed software in Matlab® in order to do the analysis of meteorological data based on the Weibull’s probabilistic distribution model and the Betz’s efficient energy use method. The software shows the wind speed and power analysis based on its data input (atmospheric pressure, temperature, wind speed and direction, etc.) of the study site. The software also allows the simultaneous and differential power analysis of different types of commercial wind turbines, which is characterized and implemented into the software, for the appropriate selection. Moreover, the user is able to upload its own meteorological files or use a data base from any weather station, such as those installed by the Regional Autonomous Corporation (CAR).
This paper describes the study and analysis of different techniques for online solar irradiance prediction algorithms to properly estimate over the 24 hours of the next day in the “Universidad Militar Nueva Granada” (UMNG) campus at Cajicá, Colombia, in order to use predictions for a model predicted control of a DC-micro grid. These models were designed and tested using MATLAB® software. The performance of models were evaluated and compared among them to determine the best forecasting approach for Cajicá. The absence of seasons and the noisy solar irradiance time series caused by cloudy covering as perturbation are the main particularity of the Cajicá’s climate behavior. A meteorological database from 2010 to 2014 was used to estimate or train the model of prediction ARMAX and NNF, NAR, NARX as Artificial Neural Networks (ANNs), which were compared with error criteria such as square and absolute error criteria.
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