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Articles by N. Albert Singh
Total Records ( 2 ) for N. Albert Singh
  S. Joseph Jawhar , N.S. Marimuthu and N. Albert Singh
  This study describes the design and development of a novel controller for a non-linear power electronic converter. Neuro-Fuzzy controller is proposed to improve the performance of the buck converter. The duty cycle of the buck converter is controlled by Neuro-Fuzzy controller. The conventional PI controllers for such converters, designed under the worst case condition of maximum load and minimum line condition, present a lower loop band width and the system response is sluggish. The common bottleneck in fuzzy logic is the derivation of fuzzy rules and the parameter tuning for the controller. The Neural Networks have powerful learning abilities, optimization abilities and adaptation. The Fuzzy logic and Neural Networks can be integrated to form a connectionist adaptive network based Fuzzy logic controller. This integrated adaptive system modifies the characteristics of rules and the structure of the control system. This paper aims to establish the superior performance of Neuro-Fuzzy controller over the conventional PI controllers and Fuzzy controllers at various operating points of the buck converter.
  M. Marsaline Beno , N.S. Marimuthu and N. Albert Singh
  Switched Reluctance Motors (SRM) is almost always operated within the saturation region for very large operation region. This yields very strong non linearity, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This study develops and compares fuzzy logic, neuro- fuzzy logic and neural network techniques for the modelling of a Switched Reluctance Motor (SRM) in view of its nonlinear magnetisation characteristics. All the models are simulated and applied for nonlinear modelling, especially for finding the rotor angle positions at different currents, from a suitable measured data set for an associated SRM. The data comprised flux linkage, current and rotor position. The model evaluation results are compared with the measured values and the error analyses are given to determine the performance of the developed model. The error analyses have shown great accuracy and successful modelling of SRMs using soft computing techniques.
 
 
 
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