Automatic tuning for a DC-DC Buck Converter with adaptive controller

The paper presents an approach based on gain scheduling technique for improving the transient performances of a digitally controlled DC-DC Buck Converter working over a large area of operating conditions. In order to determine optimal settings for the control parameters under different test scenarios, an adaptive mechanism based on machine learning algorithms is used. The experimental results, obtained after using this approach are presented as well. An improvement of 20% is observed in the case of using this gain scheduling controller instead of a controller with constant values for the parameters.