The Permanent Magnet Synchronous Motor (PMSM) is commonly used as traction motors in the electric traction applications such as in subway train. The subway train is better transport vehicle due to its advantages of security, economic, health and friendly with nature. Braking is defined as removal of the kinetic energy stored in moving parts of machine. The plugging braking is the best braking offered and has the shortest time to stop. The subway train is a heavy machine and has a very high moment of inertia requiring a high braking torque to stop. The plugging braking is an effective method to provide a fast stop to the train. In this paper plugging braking system of the PMSM used in the subway train in normal and fault-tolerant operation is made. The model of the PMSM, three-phase Voltage Source Inverter (VSI) controlled using Space Vector Pulse Width Modulation technique (SVPWM), Field Oriented Control method (FOC) for independent control of two identical PMSMs and fault-tolerant operation is presented. Simulink model of the plugging braking system of PMSM in normal and fault tolerant operation is proposed using Matlab/Simulink software. Simulation results for different cases are given.
In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learning from the BLDC motor drive and the capability of wavelet decomposition for identification and control of dynamic system and also having the ability of self-learning and self-adapting. The proposed controller is applied for controlling the speed of BLDC motor which provides a better performance than using conventional controllers with a wide range of speed. The parameters of the proposed controller are optimized using Particle Swarm Optimization (PSO) algorithm. The BLDC motor drive with RWNN-PID controller through simulation results proves a better in the performance and stability compared with using conventional PID and classical WNN-PID controllers.
Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA) board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional- integral (PI) controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker .