Development of a New Controller to Optimize Operation of a 500 Watt, Half-Bridge DC-DC Converter

This paper presents the simulation, design methodology and implementation of a Half Bridge DC-DC Converter using an optimized multi-layer feed-forwardArtificial Neural Network (ANN). The ANN controller provides an appropriate control signal to keep the output voltage of the converter stable. It was trained, using backpropagationalgorithm according to an ideal MATLAB SIMULINK model of the converter. The Ideal model is run 'off-line' to generate a record of 'perfect' control signals in response to input and output perturbations. The recorded data then are used as an 'off-line' training set fora Neural Network controller. A network pruning algorithm also is used to reduce the size of controller and an online training process performed to fine-tune thecontroller parameters. Experimental results show that the ANN controller can guarantee an acceptable load and lineregulation and provide improved performances. The proposed controller can be readily expanded to other topologies of DC converters.

کلیدواژه ها:

Intelligent ، Artificial Neural Networks ، Half Bridge ، DC converter ، Simulation