Therefore, a balanced current distribution and voltage regulation emerge as the two primary objectives when designing the control structure for a direct current MG [10]. In order to overcome the voltage deviation and current distribution challenges associated with the droop controllers, implementing a secondary control system becomes essential [2; 17]. Secondary control can be implemented in various ways, including centralized, decentralized, or distributed approaches [12; 17]. Centralized secondary control utilizes a single controller, such as a proportional-integral controller, to process the error signal. This controller's output is then transmitted to the primary control of all DERs to effectively mitigate deviations resulting from the droop control mechanism. In [18], a robust centralized controller for an autonomous direct current MG was introduced; the control was specifically developed to mitigate the impact of parameter variability in the distributed generation units and to minimize exogenous instabilities. To address the voltage regulation and energy coordination challenges in a functional distribution system, a proposed control and management approach was presented by [19] for both grid-connected and autonomous DC microgrids. In [20], a robust centralized control method for a standalone DC microgrid that takes into account the communication delay was proposed. The controller, using the Lyapunov-Krasovskii stability criteria, was intended to ensure the stability of a time-delayed DC MG using a steady and robust interpreter-based approach. The centralizedsecondary control has two drawbacks [2; 4]: susceptibility to single-point failure, leading to fatal consequences for the MG, and the requirement for a large communication bandwidth. Decentralized secondary control overcomes these drawbacks due to its lack of communication bandwidth. In this case, the control in the microgrid is accomplished by establishing voltage and current references for each DER to follow. In reference [21], a decentralized back-stepping voltage control method was developed for mitigating voltage deviations in a direct current MG. In reference [22], a decentralized controller based on artificial neural networks was presented. The controller was designed to detect and mitigate attacks on current measurements by identifying and eliminating the influence of erroneous data injection. In reference [23], a decentralized control strategy was utilized for managing photovoltaic-battery energy storage systems (PV-BES). The strategy addressed voltage regulation and control issues in situations where the PV mode dominates. A decentralized control scheme offers the advantages of simplicity and modularity in implementation, resulting in low-cost operation without the need for communication. However, the absence of a communication link restricts the global data availability, including the total power supply from all DERs, resulting in inaccurate power sharing [4]. This limitation also hampers the stability and normal functioning of DC microgrids. In recent years, due to its remarkable features, the prevalent trend is adopting a distributed control strategy for DC microgrids [2; 4; 24]. Firstly, the absence of a central controller enhances system stability and minimizes the risk of complete system collapse [24]. Secondly, using a consensus algorithm allows for a more sparsely connected communication system, reducing the costs associated with the communication network [4]. The distributed secondary control in the microgrid involves individual DER having its secondary controller. Each controller interacts with the controllers of the neighboring DERs through a communication topology to exchange information and attain consensus, creating a multi-agent system. This approach offers the advantage of maintaining functionality even if a portion of the communication network breaks down. As a result, the probability of single-point failure is significantly reduced [25].