Abstract
This paper presents the comparative analysis between fuzzy logic controller and neural network for DC-DC Buck converter. The major drawback in the conventional buck converter is when the input voltage or load change, the output voltage also changes which reduces the overall efficiency of the buck converter. So here we are using non linear controllers for buck converter which respond quickly for perturbations and maintains the fixed load voltage even when there are non-linearity’s occurs compared to a linear controllers like P,PI,PID controllers which can’t withstand when perturbations occur. Simplicity, low cost and adaptability to the complex systems without mathematical modeling are the best features of Fuzzy Logic controller and neural networks. The Two implementations are analyzed in detail and simulated in MATLAB/SIMULINK environment and results presented. Proposed approach is implemented on DC to DC step down converter for an input of 230V and performance characteristics like maximum overshoot, settling time and efficiency of the converter are studied.
Highlights
The design of dc-dc buck converter is to maintain a constant output voltage under the different load current and unregulated input voltage
The transient overshoot and recovery time of the output voltage should be minimized for stable operation in many electronic applications, which is ensured by the controller in the closed loop [1]
Neural networks are the set of algorithms inspired by the functioning of the human brain.So we are using non linear controllers for controlling the non-linearity’s in the load because it responds faster to a transient condition, easy to design and implementation
Summary
The design of dc-dc buck converter is to maintain a constant output voltage under the different load current and unregulated input voltage. Classical controllers like PWM control, proportional (P) controller, proportional integral (PI) controller, proportional integral derivative(PID) [3] controllers only provides results which is either true or false. These controllers don’t provide adequate results when there is nonlinearities in parameters or load. Non-linear controllers like fuzzy logic controllers and neural network etc are most widely used. Neural networks are the set of algorithms inspired by the functioning of the human brain.So we are using non linear controllers for controlling the non-linearity’s in the load because it responds faster to a transient condition, easy to design and implementation [4]. Here proposed controllers are used to stabilize buck converter’s load voltage in transient state conditions [6]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
