Composite control enhances stability in DC microgrids, reducing overshoot and improving performance with batteries and renewable sources.
This paper presents a novel composite control strategy aimed at enhancing large‐signal stability in DC microgrids, tackling challenges such as low inertia. The controller integrates global non‐singular fast terminal sliding mode control with a backstepping technique (BGNFTSMC) to address issues like chattering, singularity and finite‐time convergence. The microgrid comprises a solar PV system, a permanent magnet synchronous generator‐based wind turbine, a battery storage unit and DC loads, with reference values generated by artificial neural networks. The primary objective of the controller is to stabilise the DC‐bus voltage while ensuring optimal power flow regulation. To mitigate the low inertia issue, a virtual capacitor is incorporated into the design. Furthermore, a fuzzy logic‐based energy management system optimises battery endurance by managing the state of the charge and adapting to operations for reliable power distribution. The closed‐loop stability of the system is rigorously analysed using the Lyapunov stability theory, ensuring finite‐time convergence of tracking errors. MATLAB/Simulink simulations highlight the BGNFTSMC's superior performance, achieving up to 100% overshoot reduction and over 91% improvement in settling time compared to existing controllers. The adaptive neuro‐fuzzy inference system‐optimised BGNFTSMC eliminates overshoot and improves stability with a 29.76% faster settling time. The proposed BGNFTSMC controller also demonstrates excellent robustness in handling transient deviations caused by load and power fluctuations. Real‐time processor‐in‐the‐loop (RT‐PiL) experiments validate the controller's reliability. Despite MATLAB/Simulink showing improvements, including overshoot reductions of 15.789%, 21.875%, and 30.303% compared to RT‐PiL, the RT‐PiL platform maintains acceptable performance. This analysis underscores the BGNFTSMC's practical reliability.
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Islam et al. (2025) studied this question.
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