Sim-to-Real Control for Triple Active Bridge Converters in Electric Aircraft: An Online-Trained Deep Reinforcement Learning Method

The triple active bridge (TAB) enables three-port bidirectional transfer with high-frequency isolation, making it highly suitable for electric aircraft demanding efficient and lightweight power systems. However, TAB control is challenged by inherent port coupling, where disturbances at one port propagate throughout the system, and meanwhile current stress and efficiency constraints further complicate the control design. Recent advances in deep reinforcement learning (DRL) enable adaptive, model-free control but face sim-to-real limitations. To address these challenges, this paper proposes a physics-informed online-trained DRL (OT-DRL) framework for TAB converters: physical constraints are embedded into the reward function and action space, while real-time hardware data is integrated into the replay buffer to bridge the sim-to-real gap. The proposed online-trained DRL approach improves learning efficiency, transient dynamics, and ensures zero voltage switching under all conditions. Experiments demonstrate 11.3% higher episode efficiency, 2.6% greater final reward, and significant enhancements in dynamic response, 15% current stress reduction, and overall efficiency across various operating scenarios1.