###### Abstract  
In the process of integrating renewable energy sources into DC microgrids, the isolated bidirectional bridge plays a crucial role. Under load disturbances, voltage fluctuations in the microgrid can affect system stability. This study focuses on using a Genetic Algorithm to optimize the parameters of three typical DAB controllers (PI controller based on pole placement, sliding mode controller, and model predictive controller) with the aim of improving voltage stability, especially during sudden load drops. The results demonstrate that controllers optimized using Genetic Algorithm outperform the methods of pole placement and traditional manual tuning significantly. For the PI controller, the maximum drop rate reduced from 8.00% to 4.00%. The  
a stable DC bus voltage to cope with loads and other external disturbances. For DAB control, the research focuses on achieving accurate reference voltage tracking, fast output voltage dynamic response, and large-signal stability. Common control methods involve phase-shift control to regulate average power flow, with Single Phase-Shift (SPS) based on the Reduced-Order Model having been proposed [9]. Additionally, to improve efficiency, optimized phase-shift control strategies have been proposed, such as Dual Phase-Shift (DPS), Extended Phase-Shift (EPS), and Triple Phase-Shift (TPS) control, among others [10; 11]. Although these control schemes have advantages over SPS, SPS is easy to implement and convenient for use in larger multi-port DC distribution systems [3]. In terms of enhancing robustness, the introduction of nonlinear control methods such as Sliding Mode Control (SMC) is considered one of the most effective approaches, as it exhibits strong robustness and fast response to disturbances [7; 12]. By combining the advantages of SMC and DAB, it is possible to achieve precise reference tracking, fast dynamic responses, large-signal stability, and high robustness to line and load disturbances. Furthermore, in DC-DC converters, Model Predictive Control (MPC) methods have also been extensively considered, offering fast dynamic performance, ease of incorporating constraints, and simple digital implementation [13]. Finite-Control-Set Model Predictive Control (FCS-MPC) has been studied in AC power conversion [14], but its application in DC-DC converters still requires further exploration. [PERSON] et al., proposed an MPC method that calculates the optimal future control variable values using circuit information, applied in a medium-voltage naval DC microgrid considering pulse power loads [14]. However, in all DAB applications, designing the controller for the DAB converter and selecting the control parameters are crucial. The choice of control methods and parameters significantly impacts the control performance and quality of the DAB converter. For both PI control and SMC control, parameter design is primarily reliant on the pole-placement method. Nevertheless, the pole placement method is extremely sensitive to initial conditions and initial values. Even slight variations in initial values can result in vastly different control performance, challenging the robustness of the controllers. On the other hand, for MPC control, traditional manual adjustment is the prevailing approach. Manual parameter tuning typically involves a series of experiments and tests to iteratively adjust parameter values. This process is often time-consuming and may not be suitable for applications that require rapid responses. Consequently, it significantly hampers the efficiency of control adjustment. To overcome these limitations, researchers are increasingly inclined towards utilizing automated methods to fine-tune controller parameters. These methods include optimization algorithms (such as Genetic Algorithm and Particle Swarm Optimization) or adaptive control approaches. Such techniques are better equipped to adapt to system changes and uncertainties, thereby enhancing the performance and robustness of DAB control.