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Problem statement:In this study, a simplified modeling and experimental analysis of Permanent Magnet Brushless DC (PMBLDC) motors for Sensorless operation using MATLAB/SIMULINK. This model provides a mechanism for monitoring and controlling the voltage, current, speed and torque response. Approach: BLDC motor is modeled as sub-blocks. The inverter and switching function are implemented as S-function builder block. The Sensorless scheme employs direct back emf based zero crossing detection technique. Results: The proposed model with Sensorless control technique with back emf zero crossing detection is tested in the BLDC Motor and the performance was evaluated. The simulated and experimental results show that the proposed modeling works quite well during starting and running conditions. Conclusion/Recommendation: The developed model consists of several independent sub-blocks, that can be used in the modeling of Permanent Magnet Sinusoidal Motor and induction motor. Hence the developed simulation model is a design tool to study the dynamic behavior of Sensorless Controlled Brushless DC Motor.

This paper presents a simplified modeling, simulation and Experimental analysis of permanent magnet brushless dc motors (PMBLDC) for sensorless operation. This model provides a mechanism for monitoring and controlling of voltage, current, speed and torque. The sensorless scheme employs direct back emf based zero crossing detection for controlling the dynamic characteristics.

This paper deals with the Direct torque control of PMBLDC motor using hybrid controller (Genetic algorithm and fuzzy logic) and to reduce the torque ripple. Though the conventional controllers are commonly used in practice, they have failed to perform satisfactorily under non linear conditions and parameter variations. In the proposed work, a hybrid controller (using genetic algorithm and fuzzy logic controller) is introduced to control the torque and the flux linkage angle of the PMBLDC motor. Torque error and flux linkage angle of the PMBLDC motor is fuzzified and it is auto tuned by GA to improve the dynamic characteristic. Simulation results of the conventional fuzzy logic controller are compared with the hybrid (GA and fuzzy logic) controller and the later is found to be satisfactory with improved performance.

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