Brushless DC motor drive with regenerative braking using adaptive neuro based fuzzy inference system
The electronically commuted Brushless DC motors are widely used in many industrial applications which increase the need for design of efficient control strategy for these motors. This paper deals with the efficient control mechanisms for these drives using meaningful fuzzy sets and rules. The proposed system includes Brushless DC motor control utilizing the PID control, and improved performance via adaptive neuro based fuzzy control. Nowadays the electric vehicle won't be a good choice for long trips, this can be solved by our regenerative charging system since it increases the distance travelled by 50% The control strategy of regenerative braking plays an important role in maintaining the vehicle's stability and recovering energy Electric vehicles with regenerative braking can also perform Significant reduction in air pollution and optimum utilisation of energy. An adaptive neuro fuzzy inference system is developed using MATLAB. Simulation results shows that ANFIS reaches to the target faster and overcomes the complexity of the problem.
2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)