This article presents the foundations of maintenance and its different types, the level of intervention, the safety of work and devices of the wind systems, and also the types of risks that can occur during the control by blades orientation. The system under consideration is based on a wound rotor asynchronous machine excited through the rotor by means of a bidirectional converter. Control is ensured by the integration of PI. From this study, recommendations are made and simulation results are presented and discussed

Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were proposed in this paper as two models of non-linear ANN based equalization techniques in order to optimize processing performance, tracking, and minimize error of the channel effects and the ascending noise with 16 QAM Modulation, this work will be referenced with one of the most used linear adaptive equalization; Recursive least squares (RLS), as an evaluation. The two models will be compared in terms of efficiency and robustness facing noisy channel.

In this paper, an approach of trajectory tracking is proposed. The approach is based on two controls. Firstly, a quasi sliding mode control is proposed of the angular velocity in aim to converge the angle error to zero in short time with asymptotic stability. Secondly, a global sliding mode control for linear velocity is proposed, in order to bring the position error to zero and ensure the asymptotic stability by using the Lyapunov theory. Finally, the proposed control shows the performance of the algorithm, and the simulation results show good convergence for circular, sinusoidal and specific trajectories.

In this paper, a new capacitive pressure sensor (CPS) is investigated and modeled by means of neural approach. The sensing principle in our pressure sensor is based on the determination of the change in the capacity induced by the applied pressure. A ring oscillator is used to convert the capacity variation of the pressure sensor to an output frequency. A multilayer perceptron neural network is used to predict the applied pressure which causes a variation of the capacity including the temperature effects. This model is implemented as an electronic device into PSPICE simulator library, where the device should reproduce faithfully the pressure sensor behavior. Moreover, a new inverse model called smart sensor has been developed, in order to remove the nonlinearity behavior of sensor response. The obtained results make the proposed smart sensor as a potential alternative for high performances pressure sensing applications.

In this paper, Multi-depot Fleet Size Mix Vehicle Routing Problem with time window (MD-FSMVRP-TW) is presented as a multi-criteria optimization problem. For this purpose, we propose in this study a decision support system which aims to discover a set of satisfying solutions (routes) minimizing total travel distance, total tardiness time and the total number of vehicles. These routes satisfy transportation requests without contravening any of the instance specific constraints: schedules requests from clients, the heterogeneous capacity of vehicles. The new encoding and structure algorithm on which this contribution is based uses a genetic algorithm, a selection process using ranking with several Pareto fronts and an elitist selection strategy for replacement. Computational experiments with the benchmark test instances confirm that our approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MD-FSMVRP-TW problem and hence has a great potential.