Conception et commande d’un système robotisé à base de bras manipulateurs pour la palettisation en entrepôt logistique

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Energetic Performances Booster for Electric Vehicle Applications Using Transient Power Control and Supercapacitors-Batteries/Fuel Cell

In this paper, a hybrid electric power supply system for an electric vehicle (EV) is investigated. The study aims to reduce electric stress on the main energy source (fuel cell) and boost energetic performances using energy sources with high specific power (supercapacitors, batteries) for rapid traction chain solicitations such as accelerations, decelerations, and braking operations. The multisource EV power supply system contains a fuel cell stack, a lithium batteries module, and a supercapacitors (Sc) pack. In order to emulate the EV energy demand (wheels, weight, external forces, etc.), a bidirectional load based on a reversible current DC-DC converter was used. Fuel cell (Fc) stack was interfaced by an interleaved boost converter. Batteries and the Sc pack were coupled to the DC point of coupling via buck/boost converters. Paper contribution was firstly concentrated on the distribution of energy and power between onboard energy sources in consonance with their dynamic characteristics (time response). Second contribution was based on a new Sc model, which takes into consideration the temperature and the DC current ripples frequency until 1000 Hz. Energy management strategy (EMS) was evaluated by simulations and reduced scale experimental tests. The used driving cycle was the US Federal Test Procedure known as FTP-75

Hybrid Modeling for Performance Evaluation of Multisource Renewable Energy Systems

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Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle

International audienceWe present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work

Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle

<p>We present in this paper a Fuzzy Logic Controller (FLC) combined with a predictive algorithm to track an Unmanned Ground Vehicle (UGV), using an Unmanned Aerial Vehicle (UAV). The UAV is equipped with a down facing camera. The video flow is sent continuously to a ground station to be processed in order to extract the location of the UGV and send the commands back to the UAV to follow autonomously the UGV. To emulate an experienced UAVs pilot, we propose a fuzzy-logic set of rules. Double Exponential Smoothing algorithm is used to filter the measurements and give the predictive value of the errors in the image plan. The FLC inputs are the filtered errors (UGV position) in the image plan and the derivative of its predicted value. The outputs are pitch and roll commands to be sent to the UAV. We show the efficiency of the proposed controller experimentally, and discuss the improvement of the tracking results compared to our previous work.</p