Model Predictive Control of Superbuck Converter Based on Digital Triplet

Long-term operation of the power converter causes flexible changes in its state, and the model predictive control (MPC) always fails to update the predictive control model and weights to guarantee control performance. Considering the conventional digital twin (DT) structure, the virtual model is hard to simulate the physical entity with high fidelity, establishing a highly matchable predictive control model. This paper proposes a model predictive control method of Superbuck converter based on digital triplet through the optimized weight factors. The proposed digital triplet constructs a parallel triplet between digital devices in the virtual twin and physical entity to enhance the fidelity of the overall DT system. It constructs the predictive control model and weights based on the dual optimization of the virtual twin and parallel triplet, achieving a higher fidelity for the physical entity. Through the comparison of control performances on Superbuck converter between conventional MPC and DT, the proposed digital triplet empowers a more reliable and superior controller.