To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Observation of charge ordering signal in monovalent doped Nd0.75Na0.25-xKxMn1O3 (0 ≤ x ≤ 0.10) manganites

K doping in the compound of Nd0.75Na0.25-xKxMn1O3 (x = 0, 0.05 and 0.10) manganites have been investigated to study its effect on crystalline phase and surface morphology as well as electrical transport and magnetic properties. The structure properties of the Nd0.75Na0.25- xKxMnO3 manganite have been characterized using X-ray diffraction measurement and it proved that the crystalline phase of samples were essentially single phased and indexed as orthorhombic structure with space group of Pnma. The morphological study from scanning electron microscope showed there was an improvement on the grains boundaries and sizes as well as the compactness with K doping suggestively due to the difference of ionic radius. On the other hand, DC electrical resistivity measurement showed all samples exhibit insulating behavior. However, analysis of dlnρ/dT-1 vs. T revealed the clearly peaks could be observed at temperature 210K for x = 0 and the peaks were shifted to the lower temperature around 190 K and 165 K for x = 0.05 and x = 0.1 respectively, indicate the existence of charge ordering (CO) state in the compound. Meanwhile, the investigation on magnetic behavior showed all samples exhibit transition from paramagnetic phase to anti-ferromagnetic phase with decreasing temperature and the TN was observed to shift to lower temperature suggestively due to weakening of CO stat

Neural Network Local Navigation of Mobile Robots in a Moving Obstacles Environment

IF AC Intelligent Components and Instruments for Control Applications, Budapest, Hungary, 1994This paper presents a local navigation method based on generalized predictive control. A modified cost function to avoid moving and static obstacles is presented. An Extended Kaiman Filter is proposed to predict the motions of the obstacles. A Neural Network implementation of this method is analysed. Simulation results are shown.Ministerio de Ciencia y Tecnología TAP93-0408Ministerio de Ciencia y Tecnología TAP93-058

Neural network based architectures for aerospace applications

A brief history of the field of neural networks research is given and some simple concepts are described. In addition, some neural network based avionics research and development programs are reviewed. The need for the United States Air Force and NASA to assume a leadership role in supporting this technology is stressed

Identification of Nonlinear Systems From the Knowledge Around Different Operating Conditions: A Feed-Forward Multi-Layer ANN Based Approach

The paper investigates nonlinear system identification using system output data at various linearized operating points. A feed-forward multi-layer Artificial Neural Network (ANN) based approach is used for this purpose and tested for two target applications i.e. nuclear reactor power level monitoring and an AC servo position control system. Various configurations of ANN using different activation functions, number of hidden layers and neurons in each layer are trained and tested to find out the best configuration. The training is carried out multiple times to check for consistency and the mean and standard deviation of the root mean square errors (RMSE) are reported for each configuration.Comment: "6 pages, 9 figures; The Second IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-2012), December 2012, Solan

Evolving controllers for simulated car racing

This paper describes the evolution of controllers for racing a simulated radio-controlled car around a track, modelled on a real physical track. Five different controller architectures were compared, based on neural networks, force fields and action sequences. The controllers use either egocentric (first person), Newtonian (third person) or no information about the state of the car (open-loop controller). The only controller that is able to evolve good racing behaviour is based on a neural network acting on egocentric inputs