Simulation Approach for Torque Ripple Minimization of BLDC Motor

This project presents the design and implementation of neural network controller for reducing torque ripples in permanent magnet brushless DC (PMBLDCM) motor drive with trapezoidal back-emf. The performances of the proposed neural network controller are compared with the corresponding fuzzy PI controller and conventional PI controller. Simulation results are used to show the abilities and shortcomings of the proposed speed regulation scheme for brushless dc motor which is considered as a highly nonlinear dynamic complex system. Matlab/Simulink software was used to simulate the proposed model. Buck-boost converter connected in between the input DC source and three phase bridge inverter, used for minimizing the commutation torque ripples in Permanent Magnet Brushless DC Motor is presented in this paper. Torque during the commutation period depends on phase current which is not undergoing commutation, so by controlling it, torque ripple can be minimized. Moreover, a greater DC link voltage is needed during the commutation time period in comparison to the normal conduction period. The Buck-boost converter operates in boost mode in commutation period for stepping up the DC voltage to the inverter. A simple mode switching circuit is employed to amend the output modes of the Buck-boost converter in normal and commutation time intervals. Simulation studies of this topology are carried out in MATLAB/Simulink environment.