Model Predictive Control of Phase Shift Full-Bridge DC-DC Converter Using Laguerre Functions

The real-time computational load of an optimization problem plays a major role in the application of model predictive control (MPC) to fast switching power electronic converters. It is, therefore, highly desired to alleviate the computational burden of the MPC to render it feasible for these applications. In this brief, an efficient MPC algorithm based on Laguerre functions is proposed for a phase shift full-bridge (PSFB) dc-dc converter, in which the main control objective is to maintain the converter's load voltage at the desired set point while fulfilling multiple physical constraints, including the nonlinear peak input current constraint. It is shown in this work that the Laguerre functions' parameterization of MPC offers a promising solution to the high computational requirement associated with the conventional MPC without compromising on the dynamic closed-loop performance. The efficacy of the proposed control algorithm is tested on 60-W experimental hardware for 40-mu s sampling time. Experimental results are also compared with the conventional proportional-integral (PI) control structure in order to illustrate different aspects of the proposed control scheme.