Model predictive control design for DC-DC converters applied to a photovoltaic system

A continuous control set model predictive control (CCS-MPC) is designed for a DC-DC buck converter used in maximum power point tracking (MPPT) of a photovoltaic (PV) module. A modified incremental conductance (m-INC) algorithm is used for MPP determination as a reference signal for CCS-MPC. The small-signal model of the PV system is adaptively obtained around MPP through linearization of its average model. The predictive control is designed and applied to a PV system using an online optimization of the cost function including the discretized present and future states. The performance of the proposed m-INC CCS-MPC is evaluated by simulation study that indicates better performance in terms of transient and disturbance rejection compared to conventional PI controller. Finally, the applicability of the proposed m-INC CCS-MPC strategy is assessed with outdoor experimental results and the associated practical advantages against finite control set (FCS) MPC are discussed.