Robust Neural Policy Distillation of Long-Horizon FCS-MPC for Flying-Capacitor Three-Level Boost Converters

Abstract

Long-horizon finite-control-set model predictive control (FCS-MPC) can improve transient regulation and flying-capacitor balancing in flying-capacitor three-level boost converters (FC-TLBCs). However, searching over switching sequences becomes computationally expensive at high switching frequencies. We train a feedforward neural network to imitate an $N$-step FCS-MPC expert computed with beam search. To improve robustness, expert trajectories are generated under randomized input voltage, load resistance, and component parameters, and a disagreement-based DAgger variant is used to relabel on-policy states where the student and expert disagree. In simulation, the learned policy maintains stable voltage regulation and capacitor balancing under nominal conditions, operating-point changes, and perturbations of several physical parameters. We demonstrate the effectiveness of our approach by reducing the computational burden. We also demonstrate transfer to an NPC-type three-level buck converter, where initializing from the FC-TLBC network improves sample efficiency compared with training from scratch.