Enhanced DC microgrid voltage stability via adaptive synergetic control with disturbance observation and zebra optimization

• This work introduces the combined use of Nonlinear Disturbance Observer and Zebra Optimization Algorithm within a synergetic control framework. • The approach eliminates the need for a load current sensor through Nonlinear Disturbance Observer, thereby lowering hardware complexity and cost. • The manifold convergence rate is adaptively tuned using Zebra Optimization Algorithm, reducing settling time, mitigating chattering, and ensuring smoother operation. • The design maintains adaptability by updating load current and convergence speed. • Even under the most demanding operating scenarios, the maximum voltage deviation remains below 0.006%. The transition toward renewable energy is accelerating as part of global strategies to mitigate the negative impacts of fossil-based power generation. DC microgrids have become a practical platform for integrating renewable sources, offering high efficiency and flexibility. A persistent challenge in such systems is maintaining stable DC bus voltage during load changes, particularly under constant power loads that exhibit negative impedance characteristics. This study introduces an Adaptive Synergetic Control (ASC) method aimed at mitigating voltage deviations and improving system stability. The ASC combines a synergetic control framework with a nonlinear disturbance observer for real time load current estimation and the zebra optimization algorithm for adaptive tuning of manifold convergence speed. The proposed approach eliminates the need for direct load current measurement hardware, reducing system complexity and cost. Simulation results across a range of operating conditions demonstrate ASC’s ability to outperform conventional control strategies in terms of robustness and dynamic response.