<span>Due to the reliability and relatively low cost and modest maintenance requirement of the induction machine make it one of the most widely used machines in industrial applications. The speed control is one of many problems in the traction system, researchers went to new paths instead the classical controllers as PI controller, they integrated the artificial intelligent for its yield. The classical DTC is a method of speed control by using speed sensor and PI controller, it achieves a decoupled control of the electromagnetic torque and the stator flux in the stationary frame, besides, the use of speed sensors has several drawbacks such as the fragility and the high cost, for this reason, the specialists went to propose an estimators as Kalman filter. The fuel cell is a new renewable energy, it has many applications in the traction systems as train, bus. This paper presents an improved control using DTC by integrate the neural network strategy without use speed sensor (sensorless control) to reduce overtaking and current ripple and static error in the system because the PI controller has some problems like this; and reduce the cost with use a renewable energy as fuel cell.</span>

The simulation and implementation of a sliding mode control strategy for a single-phase dynamic voltage restorer (DVR) to mitigate load voltage sag swell and harmonics is presented in this work. The control strategy's goal is to compensate for the required voltage by regulating the DVR's voltage via an injection transformer while keeping the load voltage constant. The ability of the DVR to achieve a good performance greatly depends on its control strategy. The controller used in this work is based on SMC theory, which consists of creating a passivation output and a storage function to use as a function of Lyapunov. The proposed control scheme of the DVR is initially evaluated in simulations using MATLAB and validated using a laboratory-scale prototype of the entire system, including a source, the DVR circuit and a load. The control scheme is implemented on a dSPACE 1104 board and the MATLAB real-time toolbox. Both the experimental results have demonstrated the effectiveness the proposed control strategy of DVR in mitigating power qualities issues and therefore enhancing the performance of the network.