From Simulation to Control: A Digital Twin-Driven Automatic Synthesis Framework for Model Predictive Control in Power Converters

Model predictive control (MPC) is a powerful control strategy for power electronics, but it highly relies on manually derived and topology-specific analytical models, which is labor-intensive and time-consuming, often requiring days or weeks of expert effort for complex topologies. To overcome this bottleneck, this article introduces a digital-twin-based MPC (DT-MPC) framework that can systematically translate a high-level circuit into an objective-aware control policy by leveraging a DT as a high-fidelity system model. In detail, a physics-neural hybrid predictor is proposed to accelerate predictions by DT and enable real-time operation. A gradient-free simplex search optimizer is also introduced to efficiently handle complex multiobjective optimization. The efficacy of the framework has been validated through a cloud-to-edge deployment on a 1500 W dual active bridge converter. Experimental results show that the synthesized predictive model achieves an inference speed over 7 times faster than real time, the DT-MPC controller outperforms several human-designed counterparts in dynamic response, efficiency, and multiobjective optimization, and the overall framework reduces engineering design time from roughly 46 h to under 2 h in our case study. This demonstrates that DT-MPC paves the way for a generalized, automated, and high-performance control design paradigm.