A novel Neural networks controller for Buck type DC-DC converter is presented and compared with the operation of sliding mode coupled several control strategies for the converter.
The connection weights of neural network are trained by error back pr...
A novel Neural networks controller for Buck type DC-DC converter is presented and compared with the operation of sliding mode coupled several control strategies for the converter.
The connection weights of neural network are trained by error back propagation
algorithm and the trained neural network is applied to the converter control.
In this paper, each quantity is normalized in order to reduce complexity of both theoretical analysis and design for the converter.
The behavior of the control system that arises from the use of those methods is
analyzed from the viewpoint of dynamic and steady state errors and Simulation results are presented.