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

ROBUST FAULT ANALYSIS FOR PERMANENT MAGNET DC MOTOR IN SAFETY CRITICAL APPLICATIONS

Robust fault analysis (FA) including the diagnosis of faults and predicting their level of severity is necessary to optimise maintenance and improve reliability of Aircraft. Early diagnosis of faults that might occur in the supervised process renders it possible to perform important preventative actions. The proposed diagnostic models were validated in two experimental tests. The first test concerned a single localised and generalised roller element bearing fault in a permanent magnet brushless DC (PMBLDC) motor. Rolling element bearing defect is one of the main reasons for breakdown in electrical machines. Vibration and current are analysed under stationary and non-stationary load and speed conditions, for a variety of bearing fault severities, and for both local and global bearing faults. The second test examined the case of an unbalance rotor due to blade faults in a thruster, motor based on a permanent magnet brushed DC (PMBDC) motor. A variety of blade fault conditions were investigated, over a wide range of rotation speeds. The test used both discrete wavelet transform (DWT) to extract the useful features, and then feature reduction techniques to avoid redundant features. This reduces computation requirements and the time taken for classification by the application of an orthogonal fuzzy neighbourhood discriminant analysis (OFNDA) approach. The real time monitoring of motor operating conditions is an advanced technique that presents the real performance of the motor, so that the dynamic recurrent neural network (DRNN) proposed predicts the conditions of components and classifies the different faults under different operating conditions. The results obtained from real time simulation demonstrate the effectiveness and reliability of the proposed methodology in accurately classifying faults and predicting levels of fault severity.the Iraqi Ministry of Higher Education and Scientific Researc

Perancangan Dan Implementasi Sistem Pengaturan Kecepatan Motor BLDC Menggunakan Kontroler Pi Berbasiskan Neural Fuzzy Hibrida Adaptif

Mobil listrik menjadi inovasi terbaru dengan tujuan utama untuk melepaskan ketergantungan pada bahan bakar minyak. Penelitian yang telah ada memaparkan bahwa motor listrik yang sesuai untuk menggerakkan mobil listrik adalah motor Brushless Direct Current (BLDC). Beberapa keunggulan motor BLDC antara lain adalah suara halus, ukuran kompak, torsi besar, efisiensi tinggi, memiliki umur pakai yang panjang, dan mudah dikontrol. Performa dan kecepatan motor BLDC dapat terganggu apabila bekerja pada kondisi berbeban. Oleh karena itu, dibutuhkan pengaturan kecepatan menggunakan sebuah kontroler yang dapat menjaga kecepatan motor BLDC sesuai set-point meskipun sedang beroperasi pada kondisi berbeban. Kontroler yang digunakan untuk mengatur kecepatan motor BLDC adalah kontroler Proposional Integral (PI) berbasiskan Neural-Fuzzy Hibrida Adaptif. Kontroler PI dipilih karena dapat mengeliminasi steadystate error. Sedangkan Neural-Fuzzy Hibrida Adaptif merupakan kombinasi antara Fuzzy dan Neural-Network. Fuzzy digunakan untuk penentuan parameter kontroler PI. Parameter kontroler PI didapatkan dari Neural-Network. Karakteristik respon terhadap hasil implementasi memiliki settling time 20 detik, overshoot sebesar 1,1%, dan time constant 7,7 detik. ==================================================================================================================Electric cars become the latest innovations with the main objective to release the dependence on fossil fuels. Research that has been there explained that the electric motor is suitable to drive an electric car is a Brushless Direct Current (BLDC) motor. Some of the advantages of BLDC motor is smooth sound, compact size, large torque, high efficiency, has a long lifespan, and easy to control. Performance and speed of the BLDC motor can be disturbed when working on load condition. Therefore, it takes the speed setting using a controller that can keep BLDC motor speed suit to set-point even when operating at load condition. The controller used to control the speed of the BLDC motor is a Proportional Integral (PI) controller based Hybrid Adaptive Neural- Fuzzy. PI controller is chosen because it can eliminate the steady-state error. While Hybrid Adaptive Neural-Fuzzy is a combination of Fuzzy Logic and Neural-Network. Fuzzy Logic is used to determine parameters PI controller. Parameters PI Controller obtained from Neural-Network. The response characteristics of the results of the implementation have 20 seconds settling time, overshoot of 1.1%, and the time constant of 7.7 seconds

Automated steering design using Neural Network

If you don't move forward-you begin to move backward. Technological advancement today has brought us to a frontier where the human has become the basic constraint in our ascent towards safer and faster transportation. Human error is mostly responsible for many road traffic accidents which every year take the lives of lots of people and injure many more. Driving protection is thus a major concern leading to research in autonomous driving systems. Automatic motion planning and navigation is the primary task of an automated guided vehicle or mobile robots. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. In this research our artificial intelligence system is based on neural network model for navigation of an AGV in unpredictable and imprecise environment. A five layered with gradient descent momentum back-propagation system which uses heading angle and obstacle distances as input. The networks are trained by real data obtained from vehicle tracking live test runs. Considering the high amount of risk of testing the vehicle in real space-time conditions, it would initially be tested in simulated environment with the use of MATLAB®. The hardware control for an AGV should be robust and precise. An Aerial and a Grounded prototype were developed to test our neural network model in real time situation

Spectroscopic studies of oils and its synthesized bio-polymer

Nowadays, the development of alternatives to petroleum based - natural based polymeric materials were grow rapidly due to contemporary challenge attributable to environmental concerns and the effects of fluctuating oil prices.Triglycerides, the primary components of vegetable oils, are an abundant, renewable, and widely investigated as the alternative feedstock for polymeric materials [1], In this study, 3 types of cooking oil was used such as, Virgin Oil (VO), ‘Popia’ Oil (PO), and Mixed Oil (MO) for the synthesis of bio-monomer named as Virgin Oil Monomer (VOM), Popia Oil Monomer (POM) and Mixed Oil Monomer (MOM). These bio-monomers then converted to the bio-polymers named as Virgin Oil Polymer (VOP), Popia Oil Polymer (POP) and Mixed Oil Polymer (MOP). The spectroscopic properties of oils, bio-monomers and bio-polymers were tested using Fourier Transform Infrared Spectroscopy (FTIR). The characteristic peak 3010.65 cm-1 was attributed to the C–H stretching of VO, PO, and MO. The peaks at 3010.65 cm-1 disappear during the epoxidation process and new peak appear around 3330cm-1 – 3450 cm-1 in the VOM, MOM and POM attributed to the hydroxyl groups (O-H). For VOP, MOP, and POP, a strong 3330 cm-1 - 3345 cm-1 absorption band characteristic of the N–H group and an absorption band characteristic of the C=O group centered around 1700 cm-1 are present in all the FTIR spectra. Hence, its shows all types of oils were successfully converted to the bio-monomers and bio-polymers as refer to the absorption band in spectroscopic analysis

Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network

Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.Comment: 6 pages, 12 figure

Cyber-Threat Detection Strategies Governed by an Observer and a Neural-Network for an Autonomous Electric Vehicle

A pathway to prevalence for autonomous electrified transportation is reliant upon accurate and reliable information in the vehicle’s sensor data. This thesis provides insight as to the effective cyber-attack placements on an autonomous electric vehicle’s lateral stability control system (LSCS). Here, Data Integrity Attacks, Replay Attacks, and Denial-of-Service attacks are placed on the sensor data describing the vehicle’s actual yaw-rate and sideslip angle. In this study, there are three different forms of detection methods. These detection methods utilize a residual metric that incorporate sensor data, a state-space observer, and a Neural-Network. The vehicle at hand is a four-motor drive autonomous electric vehicle that is propelled using 4-pole, 3-phase Brushless DC motors. Each motor is controlled using the Direct-Torque control motor control scheme that provides fast output torque response time. This vehicle is controlled via multiple layers of control. A Model Predictive Control Layer is used to discern what lateral trajectory commands minimize the difference between the requested and actual lateral position of the vehicle. These lateral motions are discovered through a Linear-Quadratic Regulator. This study was develop using the MATLAB Simulink environment

EFFICIENCY OPTIMIZATION OF AN OPENLOOP CONTROLLED PERMANENT MAGNET SYNCHRONOUS MOTOR DRIVE USING ADAPTIVE NEURAL NETWORKS

When a Permanent Magnet Synchronous Machine (PMSM) is utilized for applications where high dynamic performance is not a requirement, a simple open loop control strategy can be used to control them. PMSMs however are prone to instability when operated open loop in a variable speed drive, particularly at mid-frequencies/speeds. This paper presents an open-loop control strategy based on a direct adaptive neural network controller is developed for efficiency optimization of open-loop controlled PMSM drive. Stability constraints of the drive system which was previously reported are used to maintain both stable and highly efficient operation of the drive system. The adopted neural network can be viewed as a method for nonlinear adaptive system identification, relying on pattern recognition of stability limits and maximum obtainable efficiency. Results from computer simulation show that a stable and highly efficient operation can be maintained for the drive system under study irrespective of load and supply variations. The obtained results are also found in correlation with previously reported experiments and observations