Model Predictive Voltage Control Strategy for Dual Active Bridge Converters Based on Super-Twisting Integral Sliding Mode Observer

The conventional model predictive control (MPC) for dual active bridge (DAB) converters relies heavily on the accuracy of system parameters. To address this issue, this paper proposes a model predictive voltage control strategy for DAB based on a super-twisting integral sliding mode observer (STISMO). By reducing the system parameter sensitivity and incorporating a disturbance compensation mechanism, the proposed strategy enhances robustness while preserving the dynamic response advantages of MPC. Firstly, an ultra-local model of the DAB converter is constructed to reduce dependence on system parameters. Secondly, a STISMO with an integral sliding surface is designed to achieve rapid and accurate estimation of unmodeled dynamics and disturbances in the ultra-local model, along with real-time compensation. The finite-time convergence of observation errors is rigorously proven via Lyapunov stability theory. Subsequently, a two-step prediction model combined with rolling optimization of the cost function is employed to solve for the optimal phase-shift angle, which is then applied in the next control cycle. Finally, experimental validation was conducted through a DAB converter test platform, demonstrating the effectiveness of the proposed method in enhancing system robustness and dynamic performance.