A Distributed Model Predictive Control Strategy for Back-to-Back Converters

In recent years, model predictive control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint, and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel distributed MPC (DMPC), which is able to achieve the same performance of the classical MPC while reducing the computational requirements of its implementation. The proposed control approach is tested on a ac/ac converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions.