Continuous Control Set Model Predictive Control of Buck Converter

Model Predictive control is a modern method of nonlinear control which gives superior performance with the cost of increased computational burden. In this paper, continuous control set model predictive control (CCS-MPC) for buck converter is proposed. This control strategy achieves constant switching frequency and retains the advantage of faster response of Model predictive control(MPC). The proposed algorithm is based on sampled data model of buck converter. The computation related to solution of a continuous optimization problem is done through a polynomial approximation of a transcendental function. The paper also shows how this approximation is valid for all practically designed buck converters. The proposed control strategy is verified in simulation and compared with experimental results, and it shows good performance for both reference tracking and disturbance rejection. It is superior compared to classical PI with lead controller and is about six times faster than conventional PI with lead controller.