电力电子系统的可微预测控制

This letter presents the first implementation of differentiable predictive control (DPC) in power electronic systems. By embedding the predictive control cost function directly into a differentiable neural network policy, the proposed approach eliminates the need for labeled control data and online optimization solvers. The control policy is trained solely from system trajectories and deployed on ...