Konstantin Kostov

Dynamic performance of buck converter with input voltage feedforward control

The effect of input voltage feedforward (IVFF) on the dynamical performance of a buck converter is investigated. The IVFF scheme is implemented making the pulse-width-modulation (PWM) ramp directly proportional to the input voltage. Ideally the IVFF control would make the internal loop gain constant, perfect attenuation of input noise and resistive input impedance with the phase of 180 deg. The transient response of the converter is, however, deteriorated due to limited duty ratio

USING INPUT INVARIANCE AS A METHOD TO FACILITATE SYSTEM DESIGN IN DPS APPLICATIONS

Distributed power supply (DPS) systems are extensively used to supply different electronic equipment and systems such as telecom switching systems. Continuous reduction of supply voltages of digita...

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 SMPCs response to line and load step changes is simulated with every combination of controller parameters emerging in GAs 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 converters continuous transfer function model is unknown, or if traditional controller design techniques do not yield satisfactory results.

Genetic algorithm controller optimization for SMPS

This paper presents a genetic algorithm (GA), which optimizes the parameters of an analog controller for switched-mode power converters (SMPCs). The GA simulates the converter with every combination of controller parameters, emerging in the population. The cost, assigned to each controller, depends on the simulated performance of the converter. An accurate and reliable simulation model is required, but knowledge of SMPCs transfer functions is not. Therefore, the proposed GA-based optimization method can be most useful when the SMPC does not have continuous transfer function model, or if traditional controller design techniques do not yield satisfactory results.