Genetic algorithm optimization of peak current mode controlled buck converter

This paper presents a genetic algorithm (GA), which optimizes the parameters of an analog controller for switched mode power converter (SMPC). A peak current mode controlled buck converter is used to test the optimization algorithm. The SMPC´s response to line and load step changes is simulated with every combination of controller parameters emerging in GA´s population. Each controller, i.e. each chromosome in the population, is assigned a cost depending on the simulated performance of the converter. The algorithm converges successfully. Although it relies on simulations, the measurements confirm that the controllers obtained by the GA result in a SMPC with stable and fast response with minimum over- and under-shoot. This method of controller optimization requires an accurate and reliable simulation model, but the transfer functions of the converter are not needed. Therefore, it can be most useful, if converter´s continuous transfer function model is unknown, or if traditional controller design techniques do not yield satisfactory results.