The Self-Tuning Triple-Active Bridge: Adaptive Control via a Continuously Updated State-Space Model for Unmatched Microgrid Performance

Abstract: Modern microgrids and energy hubs need more intelligent and flexible power converters. They want technologies that can respond dynamically, not simply move energy. The Triple Active Bridge (TAB) converter is a promising candidate for this application, but the overall efficiency is limited by conventional control methods. These techniques are based on static models or fixed-gain regulators and cannot cope with the converter's dynamic and MIMO character. This will result in slow response times, poor performance and limited adaptability. This paper therefore presents a real-time adaptive control methodology based on the converter's intrinsic dynamics: a small-signal state-space model governed by triple-phase-shift (TPS) modulation. Nonetheless, the significant transformation is in our approach; we do not simply utilise this paradigm for offline creation and then abandon it. We incorporate it into the control loop structure so that it can continuously guide and modify the phase-shift angles in real time. It is an exchange between the controller and the plant, a feedback loop that senses the changing internal state of the system. This enables power management, reduces transient overshoot and improves zero-voltage switching (ZVS) performance. We demonstrate this in two key microgrid scenarios: a single source supplying electricity to two autonomous loads, and two independent sources supplying a typical load. The results are unequivocal: our hardware prototype achieves stabilisation times of less than 0.3 seconds and maintains a conversion efficiency of over 90% in Case I and 85% in Case II. It is important to note that the controller demonstrates reliability and adapts easily to various power flow trajectories without the need for manual adjustments. This represents a significant step towards intelligent, self-adapting multi-port systems for real-world scenarios.