An efficient branch and bound algorithm for direct model predictive control of boost converter

抄録

The algorithmic efficiency offered by sphere decoding when compared with enumeration technique makes it a better choice for direct model predictive control of power converters. However, the application of sphere decoding algorithm requires that the converter's model should have the same state transition and input matrices irrespective of switch position and continuous/discontinuous conduction mode. Whereas, in power electronics we usually encounter such models, e.g. the model of the dc-dc boost converter. In this paper, we propose a branch and bound control algorithm inspired from sphere decoding for the current control of boost converter. The simulation results, verified by experimental results show that the proposed control algorithm produces same results as enumeration algorithm while being computationally efficient.