Fuzzy Direct Torque-controlled Induction Motor Drives for Traction with Neural Compensation of Stator Resistance

In this chapter, a new method for stator resistance compensation in direct torque control (DTC) drives, based on neural networks, is presented. The estimation of electromagnetic torque and stator flux linkages using the measured stator voltages and currents is crucial to the success of DTC drives. The estimation is dependent only on one machine parameter, which is the stator resistance. Changes of the stator resistances cause errors in the estimated magnitude and position of the flux linkage and therefore in the estimated electromagnetic torque. Parameter compensation by means of stator current phasor error has been proposed in literature. The proposed approach in this chapter is based on a principle that states the error between the measured current magnitude of the stator feedback and the stator’s command, verified with neural network, is proportional to the variation of the stator resistance and is mainly caused by the motor temperature and the varying stator frequency. Then the correction value of stator resistance is achieved by means of a fuzzy controller. For the first time, a combination of neural control and fuzzy control approach in stator resistance variations based on the stator current is presented. The presented approach efficiently estimates the correct value of stator resistance

Optimization of balise placement in a railway track using a vehicle, an odometer and genetic algorithm

210-214This study presents optimization of balise placements in a multi-sensor architecture for on board navigation system of a train, which runs on a track equipped with balise-based signaling system. Determination of locations of balises depends on a variety of parameters. Genetic Algorithm (GA) and Kalman filtering concept were used to find optimum places of balices in the line to reduce tachometer errors, which are one of the most important sensors in train navigation

Solving Capacitated p-Median Problem by a New Structure of Neural Network

One of the most popular and well-known location-allocation problems is the capacitated p-median problem (CPMP), which location of  capacitated medians are located to serve a set of  customer so that the total of the distance between the customers and medians (or facilities) is minimized. In this paper, first a new formulation (model) for the CPMP is presented based on two type of decision variables and  linear constraint, second based on the presented new model (formulation) is proposed a new neural network structure with five layer for solving the CPMP. The proposed neural network consist of two layers of competitive recurrent neural network with  process units, location and allocation layer, and other three layers each layer with , ,  process units respectively, which control (supervise) location and allocation layer. The useful of this proposed network is to provide feasible solutions and since the constraints are united in the neural structure instead of the energy function, therefore tuning parameters will be obviated. According to computational dynamic of new neural network the energy function (objective function) always decreases or remains constant. The effectiveness and efficiency of our algorithm for standard and simulated problems with different sizes are analyzed. The results indicate that the proposed neural network generates good quality and solutions