A Taxonomy of Robust Control Techniques for Hybrid AC/DC Microgrids: A Review

Abstract

Hybrid AC/DC microgrids have emerged as a promising solution for integrating diverse renewable energy sources, enhancing efficiency, and strengthening resilience in modern power systems. However, existing control schemes exhibit critical shortcomings that limit their practical effectiveness. Traditional linear controllers, designed around nominal operating points, often fail to maintain stability under large load and generation fluctuations. Optimization-based methods are highly sensitive to model inaccuracies and parameter uncertainties, reducing their reliability in dynamic environments. Intelligent approaches, such as fuzzy logic and ML-based controllers, provide adaptability but suffer from high computational demands, limited interpretability, and challenges in real-time deployment. These limitations highlight the need for robust control strategies that can guarantee reliable operation despite disturbances, uncertainties, and varying operating conditions. Numerical performance indices demonstrate that the reviewed robust control strategies outperform conventional linear, optimization-based, and intelligent controllers in terms of system stability, voltage and current regulation, and dynamic response. This paper provides a comprehensive review of recent robust control strategies for hybrid AC/DC microgrids, systematically categorizing classical model-based, intelligent, and adaptive approaches. Key research gaps are identified, including the lack of unified benchmarking, limited experimental validation, and challenges in integrating decentralized frameworks. Unlike prior surveys that broadly cover microgrid types, this work focuses exclusively on hybrid AC/DC systems, emphasizing hierarchical control architectures and outlining future directions for scalable and certifiable robust controllers. Also, comparative results demonstrate that state of the art robust controllers—including H∞-based, sliding mode, and hybrid intelligent controllers—can achieve performance improvements for metrics such as voltage overshoot, frequency settling time, and THD compared to conventional PID and droop controllers. By synthesizing recent advancements and identifying critical research gaps, this work lays the groundwork for developing robust control strategies capable of ensuring stability and adaptability in future hybrid AC/DC microgrids.

1. Introduction

In recent years, MGs have emerged as independent, autonomous, and scalable energy production and distribution systems, captivating electrical engineers focused on sustainable energy solutions [1]. These systems deliver a reliable and eco-friendly power source, proving especially valuable in remote locations, regions with deficient infrastructure, or areas prone to frequent blackouts [2]. MGs can seamlessly operate in islanded or grid-connected modes, enhancing resilience against disruptions [3]. A defining feature of MGs is their integration of diverse RESs, such as PV panels, which offer clean DC power but require efficient inversion for AC compatibility; wind turbines, whose variable output demands advanced forecasting and stabilization techniques; FCs, providing steady baseload power with low emissions yet sensitive to fuel purity and load fluctuations [4]. Coupled with energy storage systems like batteries, these components form a dynamic, controllable local network adaptable to varied loads [4,5]. This modular architecture enables MGs to maintain superior reliability amid grid faults or sudden load shifts. Nevertheless, harnessing decentralized and inherently intermittent RESs, such as solar’s diurnal variability or wind’s stochastic patterns—necessitates sophisticated control systems to uphold voltage and frequency stability, power quality, and seamless coordination among distributed generators, storage, and loads [6,7]. Depending on the nature of the interconnection bus, MGs are categorized into AC, DC, and hybrid AC/DC types. DC MGs, owing to their structural simplicity and high efficiency, are considered more suitable for remote and rural applications; however, they face technical challenges such as voltage fluctuations and the design of effective protection schemes. In contrast, AC MGs generally exhibit lower efficiency due to the multiple power conversion stages required to interface distributed energy resources and storage systems [4]. In HMGs, which encompass both AC and DC subgrids interconnected via bidirectional power converters, additional complexities arise, including bidirectional power flow management, domain-specific coordination, and optimal interlinking converter performance [8]. These challenges underscore the critical need for advanced, dependable control paradigms to ensure safe, reliable, and efficient HMG operation [9].

Compared to conventional AC-only MGs, HMGs introduce heightened control and operational intricacies due to their dual AC/DC architecture [10]. This stems from the imperative to regulate power flows across disparate infrastructures, compounded by the stochastic, weather-dependent nature of RESs like solar PV and wind. For instance, solar PV generates DC natively, yet most residential and industrial loads demand AC, necessitating BPCs for seamless conversion and distribution [11]. However, converter efficacy is undermined by load transients, RES unpredictability, and control latencies, potentially precipitating voltage sags, harmonic distortions, or cascading failures. Moreover, the disparate dynamics—AC systems’ sinusoidal waveforms and frequency dependencies versus DC’s steady-state voltage/current profiles—demand intricate controls for concurrent voltage regulation, proportional load sharing, and overall stability.

To surmount these in the presence of uncertainties and disturbances, HMGs rely on tailored strategies like adaptive, resilient, or hierarchical controls [12,13]. Traditional linear controllers, tuned to nominal points, falter across HMGs’ broad operating envelopes, exacerbated by model inaccuracies, component drifts (for instance filter inductances), and unmodeled nonlinearities. Robust control theory emerges as a robust antidote, explicitly accounting for parametric uncertainties and external perturbations to guarantee bounded performance. Also, external disturbances are among the most critical factors that can degrade the accuracy and efficiency of controllers. In MGs, disturbances may arise from both internal and external sources, each directly influencing system stability. Several studies have specifically focused on enhancing operational stability by mitigating or eliminating such disturbances [14].

To mitigate the escalating complexities from RES integration and dual-subgrid management in HMGs, a diverse array of robust control algorithms has proliferated [13,15,16,17,18], spanning traditional, intelligent, and robust paradigms. Traditional controllers, such as PID [19] and droop-based [20] schemes, form the bedrock of primary control layers. PID controllers excel in straightforward voltage/frequency regulation via error feedback, offering simplicity, low computational overhead, and ease of tuning for nominal conditions. Droop control, mimicking synchronous generator behavior, enables decentralized load sharing without communication, ideal for islanded modes, though it suffers from voltage/frequency deviations under heavy loads or imbalances. These model-based approaches leverage linearized plant models for predictable responses but degrade with unmodeled dynamics or parameter variations. Furthermore, recent research indicates that modern robust controllers generally demonstrate superior performance and efficiency compared to many conventional approaches. Also, increasingly, hybrid designs combine these methods to leverage their complementary strengths, ensuring stable and resilient HMG performance under dynamic conditions.

Intelligent controllers introduce bio-inspired or data-driven adaptability, circumventing rigid models. FLCs employ linguistic rules to handle nonlinearities and uncertainties, fuzzifying inputs like power errors for heuristic decision-making, thus enhancing transient response in RES-fluctuating scenarios [21]. Neural network-based controllers, including adaptive NNs, learn system mappings from data, self-tuning gains online to approximate optimal control surfaces and mitigate disturbances [22,23]. While many modern controllers for MGs rely on models, there has also been interest in model-free adaptive schemes [24]. Saverda et al. [25] provide an overview of brain emotional learning-based model-free adaptive strategies for MG control and protection, highlighting their ability to handle complexity, nonlinearity, and uncertainty better than traditional methods. They also discuss the parallels between MG hierarchical control and the brain’s emotional learning process, along with challenges, future trends, and potential applications. Advanced variants such as BELBIC are inspired by neurobiological models and mimic the processing of the limbic system to enable fast, emotionally weighted decision-making. Likewise, variants like neuro-endocrine PID [26] or CMAC [27] controllers adjust their parameters via neural or fuzzy networks in real time. These model-free approaches can handle nonlinearities and uncertainties by learning from input/output data. However, they typically lack formal guarantees and may require extensive training to perform well in all scenarios. In contrast, model-based robust controllers offer provable stability bounds under bounded uncertainty. A key gap exists between these paradigms: robust controllers usually assume a structured model, whereas model-free controllers sacrifice guaranteed performance for adaptability. Comparing robust techniques with adaptive and intelligent approaches in the context of HMGs represents a significant research gap.

Robust controllers prioritize guaranteed stability margins against worst-case uncertainties. H∞ synthesis minimizes disturbance amplification via frequency–domain norms, ideal for HMG voltage control amid parametric drifts [28]. SMC enforces invariance to matched uncertainties through high-frequency switching, ensuring finite-time convergence but risking chattering [29,30]. MPC optimizes over horizons using explicit models, balancing economic dispatch with constraints, though computationally intensive [31,32]. Also, adaptive robust variants, like μ-synthesis, fuse uncertainty bounds with online estimation for hybrid AC/DC power sharing.

The area of resilient control for hybrid AC/DC microgrids has seen significant advancements. New techniques have been developed recently to assist in increasing these MGs’ efficiency, adaptability, and stability. For AC/DC microgrids, a robust hybrid sensitivity-based control technique is presented with the goal of enhancing system performance in the face of uncertainties and disruptions. To improve stability in weak grid settings, two-way virtual inertia support is also taken into consideration while controlling the grid-forming converters [33]. Furthermore, in order to enhance operational flexibility and lessen reliance on centralized communications, distributed resilient optimization techniques have been developed for AC/MTDC hybrid power systems taking the DC grid into consideration. Comparison research using H∞ control based on the IGWO algorithm has been carried out in the area of voltage control in DC MGs, and the findings are encouraging [34,35]. The absence of established criteria for performance assessment, the limits of empirical validation, and the difficulties of integration in decentralized systems are some of the issues that still exist despite these advancements.

Although several review articles have discussed control techniques in MGs, most of them either address robust control methods in general without specific focus on hybrid configurations, or cover only AC or DC systems [36], or do not fully concentrate on HMGs and instead examine them alongside AC and DC systems [15]. Furthermore, the systematic categorization of resilient controllers based on deployment architectures (centralized, decentralized, distributed, plug-and-play) and control hierarchy (primary, secondary, tertiary) has received little attention. The main objective of this article is to provide a comprehensive review and a comparative analysis of robust control strategies developed for hybrid AC/DC microgrids, with the aim of addressing existing gaps in power systems research. This work is intended to serve as a useful reference for students and professionals interested in resilient MG control.

The main contribution of this review:

• This paper provides a comprehensive and focused review of robust control strategies specifically designed for hybrid AC/DC microgrids. Unlike most previous surveys, which either address robust control in general without focusing on hybrid configurations, cover only AC or DC systems, examine HMGs alongside conventional AC/DC systems, or discuss other control methods not strictly belonging to the robust category, this work centers exclusively on robust approaches for hybrid AC/DC systems.
• A comparative analysis of the most significant robust control schemes applied to hybrid AC/DC microgrids with different topologies and grid-connection modes is presented, highlighting their main control objectives and implementation methods.
• Several key research gaps that have received limited attention in the existing literature have been identified.
• The article proposes new research directions aimed at enhancing the reliability, efficiency, and scalability of robust controllers for hybrid AC/DC microgrids, paving the way for future advancements in this domain.

2. Hybrid Microgrids (HMGs)

HMGs are small-scale smart grid systems that co-locate AC and DC sub-networks within a single distribution framework. In these architectures the AC utility grid is linked via bidirectional converters to a DC bus hosting DC-native resources. In effect, hybrid AC/DC microgrids combine the advantages of AC and DC architectures. By allowing AC or DC-based devices to be tied in directly with minimal conversion stages, they reduce losses and simplify integration [37,38]. This makes them particularly well suited to today’s energy mix, where converter-based generation and loads are proliferating: for example, PV panels, FCs and batteries produce or store DC power, while loads such as EV chargers and electronics inherently use DC. As Unamuno and Barrena note, hybrid designs easily accommodate increasing DC-based units (EV, PV generation, FCs, ESS) while maintaining the AC-based devices on the AC network [39]. In practice, HMGs serve as platforms for embedding high levels of renewables and storage in the grid. Recent studies describe hybrid systems combining solar and wind generation, utility-scale batteries and vehicle-to-grid connected EV fleets under unified energy management [40]. These systems support bidirectional flows and flexible dispatch (using EVs as movable storage) and thus help meet smart-grid objectives for efficiency, resilience, and decarbonization. However, despite these advantages, HMGs pose several engineering challenges. Control strategies must simultaneously manage the dynamics of both AC and DC sub-networks while coordinating power sharing across a shared interface, often necessitating advanced EMSs and multilevel hierarchical control schemes. Moreover, initial capital costs remain relatively high due to the need for specialized bidirectional converters and hybrid protection mechanisms. A representative HMG configuration is shown in Figure 1.

HMGs are modeled at multiple levels of detail. At the system level, detailed simulation models (in MATLAB/Simulink or PSCAD) are used to represent the full AC and DC networks and their interlinking converters [41,42]. For example, Ortiz et al. [41] propose a 14-bus hybrid AC/DC microgrid benchmark (with separate AC and DC buses, PV, wind, storage units, diesel, loads, etc.) as a test system for power-flow and stability analysis. Jayaram et al. [42] similarly describe a hybrid configuration where the DC sub-grid contains a PV array, a wind turbine, a battery ESS and DC loads, and the AC sub-grid contains a fuel cell, another wind turbine, diesel generator and AC loads; these are tied together via a bidirectional interlinking converter (see Figure 2). Such comprehensive HMG models capture nonlinear behaviors and are used to study dynamic responses, power quality, and fault performance under realistic conditions.

At a more analytical level, researchers develop mathematical models of HMG dynamics to facilitate stability and control design. A common approach is to derive state–space models of the entire HMG by combining the equations of each converter, its control loops, and the network interconnections. For instance, Tripathy and Tyagi construct a full state–space model of a HMG by integrating multiple DC–DC and DC–AC converter models (each with their voltage, current and power controllers) and linking them via the network equations [43]. This unified model (with dozens of states) was then used for eigenvalue-based stability analysis and pole-placement controller design. In practice, such dynamic models often include the droop-controlled inverters on both sides and any relevant filters or coupling inductances. Linearizing these models yields a small-signal model of the HMG, which is widely used to assess system stability and dynamic interactions. For example, stability studies have shown that controller parameters (such as the VSC droop gains on the AC side) have a direct impact on stability margins: reducing the droop gains tends to improve stability at the expense of load-sharing accuracy [44]. Load dynamics are also incorporated—it has been reported that the behavior of dynamic loads (for instance induction machines or constant-power loads) significantly affects the low-frequency modes of the system. These analytical models (state-space and small-signal) provide insight into the stability limits of HMGs and guide the design of multi-level control schemes and robust energy management strategies [43,44]. By leveraging both modeling approaches, comprehensive simulators and mathematical model’s researchers can develop and validate advanced control and protection schemes that ensure stable, resilient operation of hybrid AC/DC microgrids.

3. Robust Control in Hybrid AC/DC Microgrids: Challenges and Approaches

3.1. Robust Control Challenges in Hybrid AC/DC Microgrids

To enhance operational flexibility, improve energy conversion efficiency, and facilitate the integration of various RESs, hybrid AC/DC microgrids are designed to leverage the complementary features of both AC and DC systems. Numerous difficulties are brought about by this integration, nevertheless, including variances in system parameters, errors in power output and consumption, and oscillations in voltage and frequency. Traditional control techniques may not be able to ensure system stability and optimum performance in such dynamic and complicated contexts. By offering system resilience against modelling mistakes, external disruptions, and variable operating circumstances, robust control has become a potent remedy for these problems [45,46]. Figure 3 shows the MG control system’s hierarchical structure.

Numerous reliable control techniques have been created to meet the unique requirements of HMGs. Backstepping control, SMC, and H-infinity control are some of the most used methods. Strong disturbance rejection is provided by H-infinity control, which works particularly well with bounded uncertainty. Due to its resilience to changes in parameters and its capacity to manage nonlinear systems, SMC is highly regarded. Conversely, backstepping makes it possible to construct nonlinear controllers in a methodical manner. These techniques, which support system stability in situations that change quickly, are often designed for power electronic converters, interlinking interfaces, and voltage/current management inside the MG [48,49,50].

In addition to conventional techniques, intelligent and adaptive robust control systems are gaining favor owing to their capacity to cope with high degrees of uncertainty and complexity. Techniques such as fuzzy logic control, neural networks, and ML-based controllers allow the system to learn from its surroundings and adjust its control approach in real-time. These controllers can dynamically react to variations in load demand, renewable generation fluctuation, and component deterioration, enhancing the overall dependability and responsiveness of the HMG. Moreover, they may be built to work in a distributed fashion, minimizing dependence on centralized control systems and boosting scalability [51,52].

Although robust control for HMGs has advanced significantly, there are still a number of obstacles to overcome. These include the necessity for precise system identification, the challenge of guaranteeing real-time implementation on embedded systems, and the trade-off between control performance and complexity. Furthermore, maintaining cybersecurity and incorporating strong control in settings with little communication are crucial issues. It is anticipated that future studies would concentrate on creating high-performance, low-complexity controllers, combining intelligent and resilient approaches, and enhancing plug-and-play functionality. In the changing energy environment, these advancements will be crucial to the realization of completely autonomous, dependable, and resilient HMGs [53,54,55].

3.2. Robust Control Approach in Hybrid AC/DC Microgrids

Robust control in HMGs ensures V/F stability, power quality, and reliable active/reactive power sharing under uncertainties, load variations, and communication delays. Hierarchical control schemes (primary, secondary, and tertiary) address distinct operational needs based on grid connectivity and topology. In grid-connected mode, active and reactive power are regulated via frequency and AC voltage adjustments, respectively, while DC voltage deviations govern power balance in DC subgrids. In islanded mode, centralized tertiary controllers are disabled to prevent instability, with synchronization loops ensuring grid alignment. Robust controllers process error signals (ΔV_AC, ΔV_DC, Δf) to mitigate disturbances, employing techniques like H-infinity or MPC. Control strategies are varied in HMGs by structure (centralized for small-scale, decentralized for large-scale), and mode (grid-connected or islanded). Droop control suits primary-level small-capacity systems, while secondary-level architectures adapt to scale. Tertiary control optimizes grid-tied energy dispatch. These strategies enhance stability margins, reduce harmonics, and ensure adaptive power sharing, making HMGs resilient and efficient. In order to enable it to respond to stiff voltage sources on either side, ref. [56] presents a transverter that is modelled after transformers connecting AC grids. For the best controller performance, it suggests model bank synthesis and employs a back-to-back converter with droop control. In [57], authors offer a current control approach for the interlink converter based on LMI. Regardless of the converter system characteristics, the interlink converter’s primary feature is its ability to allow bidirectional power exchange between the two sub-grids in the event of a power-demand imbalance in one of them. A reliable DC-link voltage and current control method for a BIC in a hybrid AC/DC microgrid is presented in [58]. It overcomes the drawbacks of traditional approaches that need distant measurement and small-signal-based control design by using backstepping and feedback linearization techniques. Using a straightforward fixed-parameter low-order controller, ref. [59] introduces a robust multi-objective controller for voltage-source-converter-based dc-voltage power-ports in hybrid ac/dc networks, guaranteeing outstanding tracking performance, robust disturbance rejection, and stability against operating point and parameter variation. The authors in [60] propose a mixed sensitivity robust control approach for grid-connected AC/DC HMGs. It changes the control of grid connections to address dispersed network disruptions. The Riccati approach may be used to produce the grid-connected robust controller, which improves system performance. A reliable, optimum coordinated control strategy for multiple voltage source converters in AC-DC distribution networks is presented in this work [61]. To guarantee system security and reduce network loss, it employs an optimization methodology. A current limit approach is also included into the system to improve accuracy and dependability. It enhances system security, according to numerical experiments. In [16], a sliding mode surface-based robust control method for a grid-connected hybrid DC/AC microgrid is proposed. By shaping the initial loop using passivity theory, the method guarantees that state variable errors converge to zero values. Under changing parameters, the method offers converter-based state variables steady mobility. A reliable ILQG controller for monitoring and dampening SN voltage in a PV-based hybrid AC-DC microgrid is proposed in this research [62]. Performance oscillations may result from the controller’s usage of an integrator to extend SN dynamics while maintaining a steady DC voltage at the SN load terminal. Using output voltage and current measurements, ref. [63] proposes a feedback control system for a hybrid bidirectional interlinking converter in an AC/DC microgrid. It suggests a strong droop control approach that takes power switching transients and load dynamics into account. A stand-alone MG that combines DG powered by RESs with local loads is presented in [64]. An intelligent control method based on fuzzy logic is suggested to preserve the stability of the DC-link voltage and frequency. An alternative to a synchronous generator is a BESS. The suggested control strategy minimizes frequency variations, cuts down on transient time, and keeps generators from operating beyond their power ratings during disruptions. In order to optimize the use of renewable power, decrease the use of conventional power, and minimize power via BPC, ref. [65] suggests a ROPMS for HMG and a robust tracking commitment for BPC. Table 1 shows the robust control for HMG analysis method. Figure 4 illustrates the different controller architectures, centralized, decentralized, and distributed control schemes. Figure 5 and Figure 6 illustrate examples of robust control applied to HMGs.

Table 1. Robust control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[56]	Decentralized	Combined	Optimization of control parameters	Optimal controller performance
[57]	Decentralized	Combined	Maintaining stability	Proper controller performance
[58]	Decentralized	Combined	DC link voltage control	Rejecting disruptive signals
[59]	Centralized	Grid connected	Rejecting a resistant disorder	Proper controller performance
[60]	Centralized	Grid connected	Improving power quality	Proper controller performance
[61]	Decentralized	Grid connected	Robustness system Improvement	Cost reduction
[16]	Decentralized	Grid connected	Dynamic modification of state variables	Sustainability assessment
[62]	Decentralized	Grid connected	Grid voltage control	Shorter sitting time
[63]	Decentralized	Combined	Power flow control	Resistant to fluctuations
[64]	Decentralized	Islanded	Maintaining DC link frequency and voltage stability	Minimizing frequency deviation
[65]	Decentralized	Combined	Power management	Optimal controller performance

Table 1. Robust control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[56]	Decentralized	Combined	Optimization of control parameters	Optimal controller performance
[57]	Decentralized	Combined	Maintaining stability	Proper controller performance
[58]	Decentralized	Combined	DC link voltage control	Rejecting disruptive signals
[59]	Centralized	Grid connected	Rejecting a resistant disorder	Proper controller performance
[60]	Centralized	Grid connected	Improving power quality	Proper controller performance
[61]	Decentralized	Grid connected	Robustness system Improvement	Cost reduction
[16]	Decentralized	Grid connected	Dynamic modification of state variables	Sustainability assessment
[62]	Decentralized	Grid connected	Grid voltage control	Shorter sitting time
[63]	Decentralized	Combined	Power flow control	Resistant to fluctuations
[64]	Decentralized	Islanded	Maintaining DC link frequency and voltage stability	Minimizing frequency deviation
[65]	Decentralized	Combined	Power management	Optimal controller performance

4. Overview of Several Robust Control Technique for HMG

4.1. Droop Control for HMG

The authors of [67] suggest a control technique for interlinking converters and investigates power sharing concerns in interconnected AC/DC microgrids. The approach consists of inner-loop data-driven model-free adaptive voltage control and outer-loop dual-droop control. Based on input/output measurement data, the controller’s design uses the Lyapunov approach to guarantee system stability. Power management in hybrid AC/DC microgrids that use solar, wind, and battery sources is covered in [68]. A PID controller and an adaptive neuro-fuzzy inference system are used to regulate the MGs. To cut expenses, an elephant herding optimization technique is used once running costs have been determined. The interlinking converter uses the autonomous droop control approach. A generalized and effective power-flow technique for hybrid AC/DC microgrids is presented in [69]. It takes into account operational factors such as voltages, frequency, coupling, unbalanced sub-grids, and slack buses. The approach sequentially solves power-flow variables, using the quadratically-convergent NR technique for decoupled equations, and models sub-grid elements in sequence components for quicker parallel solution. Power management in hybrid AC-DC microgrids that use solar, wind, and battery sources is covered in this paper. Through the introduction of a novel technique known as SDC for ac/dc HMGs, ref. [70] investigates the relationship between grid-forming droop control and virtual synchronous machine control, guaranteeing predictable, nonlinear dynamics in both networks under significant disruptions. A novel supervisory control technique for an islanded hybrid AC/DC microgrid with a high penetration of RESs is presented in [71]. In order to restore grid frequency and voltage, lower generation costs, and increase accuracy by taking reactive power impacts into account, it employs an MPC-based optimization problem. A new droop control method for a hybrid AC/DC microgrid that connects conventional and DC MGs is examined in [72]. It introduces the quasi-proportional resonance control strategy’s voltage outer loop and ideal settings. For hybrid AC/DC microgrids, ref. [73] presents a GPS-based decentralized control approach that linearly modifies output voltages in relation to per-unit output current. In order to accomplish global power sharing, it also suggests a droop control technique for the interlink converter, guaranteeing equivalent output voltage variations for AC and DC DERs. In order to perform active power filtering as well, a modified control strategy for IC is suggested in [74]. In order to filter AC side line currents and maintain a sinusoidal voltage at the AC bus under nonlinear loading situations, IC in combination with the DC subgrid may function as an APF. There has not been much research carried out on this kind of IC control in an islanded HMG yet. The power flow method for an LV hybrid AC/DC microgrid is presented in [75]. It makes use of virtual impedance and droop control principles, and its efficacy is confirmed by thorough simulation results. In order to provide plug-and-play distributed generating characteristics, ref. [76] addresses the droop control of AC and DC buses in bidirectional AC/DC converters and suggests a unique active power management technique for power balancing and independent sharing in HMGs. In order to ensure power balance and bus voltage stability, ref. [77] suggests a MG management technique that includes droop control between grid-connected and energy storage converters during operation. The energy storage converter maintains load power balance and stabilizes bus voltage after islanding. In AC/DC HMGs, interlinking converters preserves the stability of the power supply. Based on AC frequency and DC voltage, a bidirectional droop control is developed to determine power transmission power [78]. A recovery control lowers voltage drop and frequency. In DC MGs, which use interlinking converters to provide power to the AC side, voltage management is essential. To effectively manage load demand and voltage regulation among dispersed generators, droop control methods are used in [79]. By removing the need to switch between MPPT and voltage regulation modes, the suggested unified dp/dv control technique for a multi-port DC/DC converter enhances transient performance, DC bus voltage regulation, and battery SOC management. However, its validation is primarily simulation-based, with limited consideration of sensor noise, non-ideal converter behavior, and practical deployment in islanded or large-scale systems. More broadly, the literature often emphasizes modeling and simulation results over experimental or field validation, with many approaches assuming idealized network conditions. Furthermore, repeated discussion of the benefits of robust and droop control across multiple studies may obscure specific methodological insights. To strengthen the applicability of these methods, future research should focus on large-scale implementation, experimental testing, resilience under parameter variations and uncertainties, and practical integration challenges, including cyber-security and communication delays.

Table 2. Droop control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[67]	Centralized	Combined	Power sharing	DC terminal voltage recovery
[68]	Centralized	Grid connected	Power management	Proper controller performance
[69]	Centralized	Islanded	Voltage and frequency control	Proper controller performance
[70]	Decentralized	Islanded	Transient stability analysis	Improved dynamic response
[71]	Centralized	Islanded	Frequency and voltage recovery	Lower production cost
[72]	Centralized	Islanded	Safety and economic performance	High accuracy method
[73]	Decentralized	Islanded	Improving power sharing and load balancing	Fast dynamic response
[74]	Decentralized	Islanded	Active power filtering and power sharing	Efficiency of the method
[75]	Decentralized	Islanded	Power management	Performance optimization
[76]	Decentralized	Islanded	Active power control	Power balance
[77]	Centralized	Combined	Voltage stability	power balance
[78]	Decentralized	Islanded	Efficient power transmission	Frequency and voltage recovery
[79]	Decentralized	Combined	Improved DC bus voltage regulation and battery SOC control	Improved transient performance

shows the droop control for HMG analysis method.

4.2. Hierarchical Control for HMG

In order to effectively regulate the MG and create an integrated nonlinear hierarchical control and management system for hybrid ac/dc MGs, ref. [13] concentrated on nonlinear exponential control and distributed secondary control techniques. The BIC in a hierarchically controlled HMG is regulated uniformly in [80]. By eliminating commutory triggering mechanisms and system collapse brought on by imprecise or sluggish mode shifts, this method unifies control structures. A hierarchical control system for parallel power electronics interfaces in an HMG is presented in [81]. It examines both grid-connected and freestanding modes of operation. A common secondary control level to remove voltage deviation, a tertiary control level for external DC system connection, and decentralized control employing the droop technique make up the three-level hierarchical control system. The three layers of primary, secondary, and tertiary control in this paper’s proposed hierarchical control system for MGs which incorporates ISA-95 and electrical dispatching standards ensure intelligence and adaptability [82]. Power flow modeling for iHMGs, including secondary frequency and voltage restoration management, secondary voltage restoration control, and droop-controlled distributed generating units, is presented in this work [83]. In [84], the authors present a supervisory and local layer hierarchical self-regulation control approach for ESSs. For system stability, the local layer employs virtual inertia control, while the supervisory layer utilizes a cost function to regulate output power. The model includes an FC, hydrogen storage tank, and electrolyzer. A sturdy hierarchical control architecture for a hybrid shipboard MG system with many DGs and integrations of renewable energy resources is shown in [85]. In addition to performing system stability analysis and control law design, it verifies the design’s performance against noise and undesired load situations. A hierarchical and distributed control approach for clusters of AC and DC MGs linked by a flexible DC distribution network is presented in [86]. A framework including DG-layer, MG-layer, and CC-layer layers is described, and two control mechanisms are suggested for varying voltage needs. In order to maintain low THD states, minimize the influence of power quality, and anticipate operational states in advance, ref. [87] presents a quicker model predictive optimization method for primary control. Secondary switching control is then implemented. While these methods demonstrate significant performance improvements, challenges remain in managing parameter variations, nonlinearities, and uncertainties, as well as ensuring real-time implementation and practical scalability. Overall, the studies provide valuable insights into control design and coordination mechanisms in hybrid AC/DC microgrids.

Table 3. Hierarchical control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[13]	Decentralized	Combined	High power quality	Better controller performance
[80]	Centralized	Combined	BIC uniform control	Stable performance
[81]	Decentralized	Combined	DC load current sharing	Three-layer control function
[82]	Decentralized	Combined	Voltage deviation recovery	Effectiveness of the method
[83]	Centralized	Islanded	Power stability assessment	Decent speed and convergence rate
[84]	Centralized	Islanded	Economic optimization	Reduce operating costs
[85]	Centralized	Combined	Strength of hierarchical control	Noise resistant
[86]	Decentralized	Combined	Coordination between linked inverters	Effectiveness of the method
[87]	Decentralized	Combined	Reactive power stability	THD reduction

shows the hierarchical control for HMG analysis method.

4.3. H_∞ Control for HMG

In order to improve stability and tracking precision, ref. [88] suggests a $H_{\infty }$ robust control approach for bidirectional converters. Through Park transformation, the technique lowers system order, simplifying controller design and lowering the complexity of phase angle tracking. In [89], a robust frequency control for islanding provisional MGs is proposed, focusing on hybrid AC/DC and AC conventional parts. The optimal controller is determined using an algorithm and sensitivity functions, despite lack of scientific reports. Ref. [90] proposes a coordinated control method for an AC/DC HMG, dividing control into three flexible switching modes to meet power quality requirements and achieving precise control of frequency and voltage based on optimal distributed coordinated control theory. Two adaptive control techniques are presented in [91] for the smooth transition between islanded and grid-connected modes as well as for MG voltage and frequency adjustment [82] in islanded mode. The controllers use MPC and $H_{\infty }$ to enhance droop control performance. The P/Q control technique is used to modify the transmission of active and reactive electricity when linked to the utility grid. Integral of square error, integral of absolute error, and integral time-weighted absolute error are used as the basis for comparisons. The study presented in [92] describes a control strategy that uses droop control and $H_{\infty }$ robust control to modify the voltage and frequency of an MG in islanded mode under various loading scenarios. Four steps make up the method: an LCL filter and coupling circuit, a droop control loop, a voltage control loop, and a current control loop. In order to support the traditional droop control approach, ref. [93] suggests an $H_{\infty }$ control method based on HS. The V/F controller’s performance is improved by using the suggested technique. It can improve autonomous MG power quality while controlling voltage and frequency to their regulated levels. In order to understand the hybrid interacting behaviors of MGs, ref. [94] presents a unique hybrid model. It suggests a two-level hierarchical hybrid control system that alternates between discrete management and continuous controllers. Stability, security, load demand, cost reduction, and emission reduction are all guaranteed by the upper-level discrete management. Nevertheless, challenges remain in managing system nonlinearities, parameter variations, and the computational complexity of implementing predictive and hierarchical controllers in real-time large-scale systems. Future research should focus on developing scalable, intelligent, and adaptive control schemes that maintain robustness under variable operational conditions, optimize energy dispatch, and address practical considerations such as communication delays and cyber-security, thereby translating these sophisticated strategies into reliable, real-world HMGs.

Table 4. $H_{\infty }$ control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[88]	Centralized	Grid Connected	Voltage stability	Improved phase angle accuracy
[89]	Centralized	Islanded	Frequency control	Determining the weighting function
[90]	Decentralized	Combined	voltage control	Achieving dynamic power balance
[91]	Decentralized	Combined	Accurate voltage and frequency regulation	Improved dynamic response
[92]	Centralized	Islanded	Improve voltage and frequency regulation	Reduced disturbance
[93]	Decentralized	Islanded	Enhance MG power quality	Demonstrated superior V/F regulation and improved power quality
[94]	Decentralized	Combined	Model and control the hybrid dynamic behaviors of MGs	Minimize operational cost

shows the $H_{\infty }$ control for HMG analysis method.

4.4. Distributed Control for HMG

In order to offer appropriate voltage and frequency controls as well as power sharing, ref. [95] presents a unique hierarchical control strategy for MGs that combines main and secondary layers for cooperative voltage and frequency secondary control approaches. A new distributed coordination control technique for many SMGs in a hybrid AC/DC microgrid is presented in [96]. By eliminating reliance on particular variables for power exchange, this technique guarantees complete controllability for interlinking converters. Additionally, it ensures continuity and control of power supply even in the event of a single SMG failure. In [97], iPEBB for hybrid DC/AC microgrids are proposed as a distributed control option for modular power converters. Every iPEBB functions autonomously, but a central controller oversees the system as a whole, assigns roles to each iPEBB, and accomplishes control objectives. In order to govern power flow across hybrid AC/DC microgrids, ref. [98] presents a novel distributed coordinated control technique for numerous ICs. The system mitigates circulating current, controls DC voltage for DC SMGs, and enables dependable management and regulation of power flow between SMGs. The altered outer control loop minimizes circulating current at the DC side and guarantees precise power sharing. Using variable control schemes and good dynamic responsiveness, the project [99] presents an MPC-based distributed control algorithm that determines BIC to govern the most deviated parameter in nonlinear settings after analyzing grid conditions. A new distributed active power control technique for interlinking converters in an IHMG is presented in [100]. It seeks to establish power sharing without the need for extra controllers. A unified state-space model is provided together with an estimator and a mathematical formula for active power references. In order to maximize the use of RESs, ref. [101] suggests an EMS for an HMG network. To provide precise load demand forecast and appropriate management during power-sharing periods, the system employs integral controllers and proportional resonance to handle DG and PV sources. In order to distribute power efficiently, ref. [102] suggests a distributed control approach for a hybrid AC/DC microgrid that makes use of a higher control layer and the adaptive droop technique. Distributed generating units, converters, energy storage devices, and RESs make up the system. In [103], a distributed coordination control approach that takes SOC storages into account is proposed for numerous BPCs in a hybrid AC/DC microgrid. The suggested approach increases the complexity of the control technique while guaranteeing system dependability. By taking into account both AC and DC subgrids, it improves the HMG’s dependability while making the control method more intricate. With an emphasis on cost-effective operation, ref. [104] suggests a unified distributed control strategy for hybrid AC/DC microgrids. Consensus among sources, average voltage recovery, and voltage observers are all part of the plan. The bidirectional DC-AC interlinking converter’s reference power is set by the PI controller, and worldwide incremental costs are equalized in accordance with the corresponding protocols.

For increased dependability and lower communication costs, ref. [105] suggests a locally-distributed and globally-decentralized MG control system that makes use of local sparse low-bandwidth communication networks. Flexible controllers for connecting and integrating converters in hybrid AC/DC microgrids are suggested by [106]. These controllers minimize droop power flow and system stability problems by being made for several stacked bidirectional DC-AC ICs/IFCs outlays. They concentrate on controlling the wide-spread AC/DC bus characteristics, which guarantees system stability under a variety of operating circumstances. A centralized battery energy stack is included into the suggested HMG for high-power transfer efficiency. In [107], authors use a hierarchical distributed cooperative control technique for both clusters inside and between micrography to address electricity sharing concerns in AC/DC HMG clusters. A distributed control approach for a hybrid MG with linked AC and DC subgrids is examined in [108]. By combining LEC with GEC, the technique lowers implementation costs, increases algorithm convergence time, and improves operation dependability. Bidirectional interlinking converters connect the linked subgrids, guaranteeing worldwide economic activity in compliance with the equal IC concept. In order to achieve global system economic operation, ref. [109] suggests a distributed control architecture for a hybrid AC/DC microgrid. There are two layers to the architecture: DC bus voltage-IC droop and AC frequency-IC droop. By offering a universal approach for fac and Vdc recovery, the DCCF improves system scalability and lessens communication constraints. To remove hidden loading conditions and guarantee that all DG ICs converge to the same value in the steady state, an original RLI is suggested. The work described in [110] integrates a pulse load and suggests a new power flow management technique for a hybrid AC-DC microgrid that uses energy storage and solar power. With a DC-DC boost converter, synchronous generator, and PV farm, the system runs in islanding mode. A framework for the best operation management of hybrid AC-DC microgrids that includes battery storage and both dispatchable and non-dispatchable energy sources is presented in [111]. The framework makes use of the ADMM, multi-agent mechanism, and distributed consensus-based structure. A novel optimization technique based on the CSA is created for the best local solutions since the objective function is nonlinear. To increase power distribution and dependability, a control technique using particle swarm optimization and energy management algorithms was put out [112]. In order to support the HESS, the strategy featured an auxiliary power control unit and was based on the load profile and power generating resources. High-frequency and low-frequency components made up the net power. Through power sharing, power exchange, and power management, the study suggests a distributed control strategy for a hybrid three-port AC/DC/DS microgrid, allowing for dependable autonomous operation. This plan [113] incorporates multi-level power exchange control to minimize needless power exchange and extend storage lifespan, as well as decentralized control that enables each power module to function independently. A distributed control approach for hybrid cascaded-parallel MGs that integrates many low-voltage power sources is presented in [114]. For increased system redundancy, it adds a sign function, active and reactive power regulators, and a low bandwidth communication network.

Table 5. Distributed control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[95]	Decentralized	Combined	Improved voltage and frequency regulation	Power sharing
[96]	Decentralized	Combined	Power flow management	Continuity of power transmission
[97]	Centralized	Combined	Independent control and central management	Efficiency and flexibility of the structure
[98]	Decentralized	Grid Connected	DC power current and voltage regulation	Reduce circulating current
[99]	Decentralized	Grid Connected	Voltage and current regulation	Setting parameters
[100]	Decentralized	Islanded	Active power control	Power sharing
[101]	Centralized	Combined	Energy management system	Freight demand forecasting
[102]	Centralized	Combined	Reliable and efficient performance	Optimal energy distribution
[103]	Decentralized	Combined	Power management	Increased reliability
[104]	Decentralized	Combined	Economic optimization of the system	Voltage improvement
[105]	Decentralized	Islanded	System performance optimization	Reducing communication costs
[106]	Decentralized	Grid Connected	Improve system reliability and performance	Voltage control and power sharing
[107]	Decentralized	Combined	Improving power and voltage quality	Stability against disturbances
[108]	Decentralized	Grid Connected	Optimizing the economic distribution of power	Economic performance
[109]	Decentralized	Grid Connected	Reducing operating costs	Reducing communication load
[110]	Decentralized	Islanded	Improve power management	High efficiency
[111]	Decentralized	Islanded	Optimal operations management	Optimal convergence
[112]	Decentralized	Combined	Power distribution optimization	Increased reliability
[113]	Decentralized	Islanded	Power sharing between networks and storage	Reducing unnecessary power exchange
[114]	Decentralized	Islanded	Effective power sharing	Small signal stability

shows the distributed control for HMG analysis method.

4.5. SMC for HMG

In order to improve hybrid AC/DC microgrid stability and power sharing, especially for nonlinear and unbalanced loads, ref. [115] presents a decentralized resilient method. Two controllers for positive and negative sequence power and current control are included; they are based on Lyapunov function theory and SMC. In order to reduce outside disruptions and cyberattacks, the study [116] presents a fuzzy SMC technique for voltage regulation in an islanded AC/DC HMG. It makes use of an integrated SMC and the T-S fuzzy model. Using a wind-driven generator, solar module, battery storage, power converters, and an SMC for optimal power extraction, ref. [117] investigates the utilization of wind, solar, and battery energy as major sources for HRES. In [118], the authors suggest a hybrid AC/DC microgrid that combines 4.5 kW solar and 8 kW wind systems with an ASMC based on a barrier function. Notwithstanding disruptions, the system guarantees output variable convergence. In order to provide DC bus voltage management during islanding and AC/DC link bus voltage regulation during grid-connected mode, control rules are defined using global mathematical modeling. An effective and cost-effective setup for a wind/solar PV system coupled with a BESS is proposed in [119]. It suggests a GSMCFO to improve the HMGs robust, steady-state, and transient performance. The controller maintains power balance, controls DC-link voltage, guarantees appropriate power transmission, and tracks maximum power points. The isolated wind-diesel HMGs suggested coordination control approach lowers demand variance and frequency deviation from changes in renewable energy. For diesel and wind turbine generating systems, it employs the sliding mode approach and a smart neural network observer. By taking load fluctuation into account and using an adaptive neural network observer, it increases control precision [120]. In order to govern hybrid smart MG power systems under uncertainty, ref. [121] proposes an ASMC that uses fuzzy logic. The Lyapunov theory is used to guarantee the stability of the controller.

Table 6. SMC for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[115]	Decentralized	Combined	Dynamic stability	Improved power control
[116]	Centralized	Islanding	Reducing the chattering phenomenon	Ensuring sustainability
[117]	Centralized	Combined	Extraction of Maximum power	Comparison with P&O algorithm
[118]	Centralized	Islanding	Ensure robust voltage regulation under disturbances without prior knowledge of disturbance bounds	Voltage stability
[119]	Decentralized	Grid-connected	Improve dynamic and steady-state response	Achieve robust MPPT for PV and wind
[120]	Decentralized	Islanding	Reduce frequency deviation	Improved output regulation
[121]	Centralized	Combined	Ensure system stability under uncertainties	Reduce chattering and improve power quality

shows the SMC for HMG analysis method.

4.6. Other Method Control for HMG

A backstepping method for creating controllers for hybrid AC/DC microgrids is presented in [122]. Solar photovoltaics, wind turbines, BESSs, and loads are all part of the DC-side. A synchronous generator and a bidirectional voltage source converter are used in the AC portion. In order to maintain voltages and guarantee power sharing, the controllers are decentralized. Control Lyapunov functions are used to theoretically examine the MG’s stability. Using a neutral point clamped NPC converter to manage voltage and AC currents and maintain a power factor close to unity, ref. [123] investigates the use of a novel backstepping predictive technique to control the DC and AC components of HMGs. In [124], a hierarchical control system that permits a small DC voltage variation for bidirectional power transmission in a hybrid AC/DC microgrid is introduced. Additionally, it explores how electric car involvement affects reactive power management and converter capacity reduction, and it proposes a reactive power control method. A distributed coordination control approach for many BPCs in a hybrid AC/DC microgrid is presented in [125]. The technique allows the two sub-grids to sustain one another in both grid-connected and islanded modes by facilitating suitable power interaction between them. Three enhancements include using DC droop control for DC current sharing, managing the square of DC voltage for linearized control, and suppressing AC circulating current using a d-q-0 three-axis control method. The study presented in [126] suggests a distributed coordination control approach for hybrid AC/DC MGs that uses a distributed consensus method to restore AC frequency and DC voltage to nominal values, maintain power sharing, and regulate correct DC current and reactive power sharing. Reference [127] suggests an online optimum control technique for HESSs in AC-DC MGs that is based on reinforcement learning. By addressing irregularities in uncontrolled charging and discharging, this technique enhances power efficiency and quality. The C&D profile is optimized and disturbances are suppressed by using the optimum control theory. The work presented in [128] trains models using LSVR, Matern 5/2 GPR, and rational quadratic GPR utilizing historical climate data from Islamabad, Pakistan. A power scheduling control method is put into place once the models are examined using RMSE. In [12], a hybrid AC/DC microgrid powered by wind turbines and PVs is examined. It creates and verifies a novel control system that uses fuzzy logic and adaptive neural networks to reduce electrical grid energy usage. The use of FC and PV as RESs is investigated in [129]. It suggests a DNN-based technique for an enhanced MPPT controller. Despite possible power losses, the objective is to enhance the output power quality in FC and hybrid PV systems. Also, Islam et al. [130] propose a robust composite control strategy for hybrid AC/DC microgrids, combining non-singular integral terminal sliding mode and nonlinear backstepping with adaptive fractional-order reaching law, ANN-based MPPT, and ANFIS tuning. The approach achieves finite-time stability, suppresses chattering, and improves transient response with up to 86% overshoot reduction and 78% faster settling under dynamic uncertainties.

Table 7. Other methods of control for HMG analysis method.

Ref.	Structure	Operating Mode	Objective	Results and Metrics
[122]	Decentralized	Combined	Voltage control	Sustainability analysis
[123]	Decentralized	Combined	DC voltage regulation	Harmonic distortion reduction
[124]	Decentralized	Combined	Power management	Reduction of nominal capacity of interface converter
[125]	Decentralized	Combined	Reducing eddy currents	Power improvement
[126]	Decentralized	Combined	Maintaining the balance of power	Power subscription management
[127]	Centralized	Combined	Suppress disturbances caused by fast charging/discharging of HESS	Improve transient performance
[128]	Centralized	Islanding	Controlling power flow and maintaining battery reserve levels in real-time conditions,	Reducing frequency fluctuations
[12]	Centralized	Grid-connected	Accurate tracking of maximum power point for RESs.	Reducing power consumption from the main grid (reducing energy costs).
[129]	Decentralized	Combined	Improving the quality of generated power in hybrid solar and FC systems.	Reducing power fluctuations and improving the overall quality of generated power.
[130]	Decentralized	Combined	Enhancing transient stability and power balance in hybrid DC/AC microgrids under uncertainties and disturbances	Improved stability margins (reduced oscillations by 30–50%), faster settling times, robust power sharing with minimal voltage/frequency deviations, validated via simulations.

presents the control strategy categorized under other methods used in the analysis of the HMG system.

5. Discussion

Intermittent energy sources, nonlinear loads, and a variety of management designs provide major obstacles to preserving system stability, power quality, and overall dependability in hybrid AC/DC microgrids. In order to handle the inherent uncertainties and external disruptions in such systems, robust control has become a sophisticated and scientifically supported method. Methods like mixed-sensitivity design, SMC, and H∞ control provide a high tolerance to measurement noise, time delays, and parameter changes. Without needing a precise model of every system component, these techniques allow the creation of controllers with strong disturbance rejection capabilities, large stability margins, and quick dynamic response. While each category of robust control techniques has been independently reviewed in the preceding subsections, a direct comparative analysis is essential for practitioners to select suitable methods under different operational contexts.

Table 8. Comparison of several robust control strategies: complexity, adaptability, communication, suitability.

Control Strategy	Robustness to Uncertainties	Computational Complexity	Real-Time Suitability	Communication Dependency	Suitable Operating Modes
SMC	Very High	Medium	High (with tuning)	Low	Islanded, Fast-varying loads
$H_{\infty }$ Control	High (bounded disturbances)	High	Medium-Low	Medium	Grid-connected
Droop Control	Medium	Low	Very High	Very Low	All (especially Islanded)
Hierarchical Control	Medium	Medium	Medium	Medium	All (with centralized EMS)
Distributed Control	Medium-High (topology tolerant)	High	Medium	Medium-High	Scalable, Multi-agent HMGs

summarizes key features of each control strategy, including implementation complexity, robustness level, adaptability to uncertainties, communication requirements, and suitability for different MG modes.

Research has shown that interlinking converters, which act as the crucial interface between AC and DC subsystems, benefit greatly from strong control tactics. Their use greatly enhances the MGs dynamic responsiveness, voltage stability, and power flow management. Furthermore, the system’s performance is improved under a variety of operational scenarios, including islanded mode, grid-connected mode, and power transfer conditions, by incorporating robust controllers into hierarchical control structures. Figure 7 shows classification of robust control strategies based on design philosophy and adaptability.

Furthermore, the next generation of intelligent robust controllers is represented by new hybrid techniques like robust ML-based control and adaptive robust control. Without requiring human retuning, these techniques adjust in real time to significant changes in the environment and system uncertainty. Under changing operating circumstances, they provide optimum and real-time performance by using technologies such as neural networks, fuzzy logic, and reinforcement learning algorithms.

In summary, the use of intelligent and resilient control strategies that concurrently guarantee system stability, efficiency, and dependability has become essential due to the growing complexity of HMG operation. This is a crucial and exciting topic for future MG research as designing and implementing such controllers calls for precise modelling, a deep understanding of system dynamics, and the application of sophisticated analytical techniques.

Table 9. Comparison of robust control strategies.

Control Strategy	Advantages	Challenges
H-infinity control	Strong worst-case performance; frequency-domain tuning	Requires accurate models; complex computation
SMC	High robustness to parameter changes and nonlinearities	Chattering effect; implementation difficulty
Mixed-sensitivity design	Balances robustness and performance; systematic design	Model-dependence; limited experimental validation
Adaptive robust control	Adjusts to unknown parameters in real-time	Stability guarantees can be hard to prove; slow convergence
Fuzzy + Robust control	Handles uncertainty and vagueness; intuitive logic	Tuning rules are complex; lacks general stability proofs
ML-based robust control	Learns optimal policies; no need for full model	Requires large data; lack of transparency; hardware resource constraints

presents a comparative analysis of robust control strategies for HMGs, evaluating their performance across key technical parameters.

Although robust control systems have theoretical benefits, there are a number of practical obstacles to overcome before they can be used in HMGs. Scalability is still a big problem, particularly when using decentralized or distributed controllers in large-scale systems. Performance in hierarchical or multi-agent systems may be severely hampered by communication delays and data packet losses, especially at the secondary and tertiary control levels. Furthermore, the real-time application of sophisticated optimization or ML-based controllers may be limited by the processing limitations of embedded systems.

A review of the literature indicates that most robust control methods for hybrid AC/DC microgrids remain at the stage of algorithm design or simulation, highlighting a distinct lack of experimental validation in this field. In practice, however, several non-ideal operating conditions arise that significantly affect controller performance and practical feasibility.

Table 10. Practical implementation challenges for robust control in hybrid AC/DC microgrids.

Challenge Category	Specific Challenge	Description and Root Cause	Implications on System Performance	Potential Mitigation Strategies
Sensing and Measurement	Sensor Noise and Bias	Voltage and current measurements from transformers (VTs/CTs) and transducers are corrupted by inherent thermal noise, scaling errors, and DC offsets, particularly in the noisy electromagnetic environment of power electronic converters.	Degrades control accuracy, leading to steady-state voltage oscillations, inaccurate power sharing, increased harmonic distortion, and potential false protection trips.	Employ advanced filtering techniques (for instance Kalman Filters, adaptive notch filters), use higher-precision sensors, and implement online calibration routines.
Communication Infrastructure	Communication Delays	Latency and jitter inherent in wireless (Wi-Fi, 5G) and wired (Ethernet, CAN bus) networks used for distributed control, exacerbated by network congestion and protocol overhead.	Introduces phase lag in control loops, reducing stability margins and damping performance. Can cause system-wide oscillations, deteriorate frequency regulation, and lead to cascading failures.	Design delay-aware robust controllers (H∞ methods with delay compensation), implement network traffic shaping, and use predictive control schemes like MPC.
	Limited Bandwidth	Constrained data throughput in cost-effective or legacy communication networks restricts the amount and frequency of data exchange between distributed controllers.	Results in slower dynamic response to disturbances, inaccurate state estimation, and weaker coordination among DERs, undermining system resilience.	Utilize data compression algorithms, implement event-triggered communication protocols (instead of time-triggered), and optimize network architecture for critical data streams.
Computational Resources	Computational Complexity	Advanced control algorithms (Model Predictive Control-MPC, deep reinforcement learning) require solving optimization problems or processing large neural networks within short sampling times.	Often infeasible for low-cost, commercial off-the-shelf microcontrollers, leading to missed sampling deadlines, controller failure, or the need for expensive high-performance hardware.	Develop simplified and reduced-order models for controllers, employ edge-cloud computing hierarchy, and design hardware-efficient algorithms tailored for FPGA or DSP deployment.
Hardware Constraints	Processor and Memory Limits	Embedded processors, FPGAs, and edge devices in MGs have constrained clock speeds, RAM, and flash memory, limiting the sophistication of executable code.	Forces a trade-off between control performance and implementability, often resulting in the use of simplified, less robust controllers (e.g., droop control instead of optimal strategies).	Optimize code for efficiency, leverage hardware-specific accelerators, and employ distributed computing where different control layers run on separate dedicated hardware.
	Converter Switching Dynamics	Imperfections in practical power converters (e.g., dead-time effects, nonlinearity, switching delay) are often idealized in simulations but present significant challenges in hardware.	Causes harmonic instability, degrades voltage quality, and introduces non-ideal behaviors that can defeat theoretical control strategies designed on linearized models.	Implement adaptive dead-time compensation, use repetitive controllers for harmonic suppression, and employ robust controllers designed to be insensitive to model uncertainties.
Implementation and Validation	Real-Time Testing Hurdles	The high cost and expertise required for Hardware-in-the-Loop (HIL) simulation and lab-scale prototyping create a barrier for experimental validation of complex controllers.	Perpetuates the gap between theoretical algorithms and proven, deployable solutions. Lack of standardized testing protocols makes performance comparison difficult.	Promote open-source HIL platforms, develop standardized benchmark MG models for testing, and create detailed guidelines for moving from simulation to implementation.

summarizes the main challenges, their implications, and related references. However, some studies have established real-world or HIL platforms to evaluate the performance of robust control. For example, Espina et al. [131] developed a hybrid AC/DC microgrid in a university laboratory consisting of three AC three-phase converters and six DC converters. By employing a distributed control approach for frequency regulation and load management, they demonstrated that after each load disturbance, the system frequency returned to its nominal value and transmission line congestion was relieved within a few seconds. Moreover, in simultaneous AC and DC load tests, the consensus algorithm for active/reactive power sharing and average voltage regulation successfully achieved convergence in both AC and DC subsystems, thus maintaining overall system stability [131].

Similarly, Rehmat et al. [132] implemented a robust hierarchical control scheme combining supervisory control with SMC in a shipboard HMG (laboratory emulation). Hardware experiments on an NVIDIA Jetson Nano board showed that under a load increase scenario (50%→80%), the frequency and voltage regulation errors remained below 2.5% and 3%, respectively. In addition, the AC bus voltage THD was about 3.8% under steady-state conditions, peaking at 5.1% but quickly stabilizing, indicating the effectiveness of the robust controller in preserving power quality.

Complementarily, emerging certifiable AI methods aim to embed safety certificates (such as Lyapunov-barrier functions) within controller design to guarantee stability and constrain behavior. Finally, closing the loop with actual hardware through more hardware-in-the-loop tests and standard benchmark problems is essential to build confidence. In summary, bridging the simulation–reality gap and establishing common standards are key steps toward enabling trustworthy AI control in HMG [133].

6. Future Work

A brief review of HMGs’ architecture and a review of robust control strategies for HMGs have been performed in this paper. In recent years, significant progress has been made in the field of robust control for HMGs, but there are still challenges that will provide the basis for future research. One of the most important research directions is the development of ML-based robust controllers with real-time adaptive capabilities, so that these controllers can maintain optimal performance and system stability in the face of changing environmental conditions and dynamic uncertainties without relying on accurate models [23]. Also, designing distributed robust control strategies that can cope with communication delays, link degradation, and scalability of MG structures is another important research priority [134]. Various types of cyber-attacks can compromise system reliability and performance. Among these, DoS and FDI attacks are among the most prevalent threats. On the other hand, in today’s world where cyber-attacks in energy systems are increasing [135], the need to improve cyber resilience in resilient control systems is strongly felt. To mitigate these risks, dedicated secure-control frameworks have been proposed. Kounev et al. [136] advocate grouping control devices into protected enclaves with cryptographic authentication across their communications. Similarly, Angjelichinoski et al. [137] embed an in-band power-talk handshake in the DC control loop to authenticate distributed energy resources. Industry standards (IEC 62351) recommend using digital signatures and hashing for message integrity. Recent work also leverages digital-twin simulators as virtual testbeds to expose controllers to cyberattack scenarios and evaluate resilience [138]. Together, these approaches—combining encryption, authentication, anomaly detection, and redundant control paths—form a secure-control paradigm to maintain HMG stability in adversarial environments.

Looking ahead, we propose concrete research directions to integrate cybersecurity into MG control design. One key avenue is secure distributed control: for instance, developing consensus-based secondary controllers that can detect and isolate compromised agents while preserving power-sharing objectives [139]. Embedding lightweight cryptographic protections (such as message authentication codes, hash-based signatures, or post-quantum schemes) into control communications is also critical to ensure data integrity without violating real-time constraints [136]. Importantly, these strategies must be validated on realistic platforms: HIL testbeds—for example OPAL-RT based CHIL setups—can emulate actual MG hardware and inject coordinated cyber-attacks to rigorously assess controller robustness [139]. Complementary use of digital-twin and co-simulation environments would enable systematic stress-testing of control algorithms under diverse cyber-physical scenarios. Pursuing these directions—secure distributed consensus control, cryptography-based resilience, and rigorous HIL/digital-twin validation—will help bridge the gap between theoretical robust-control designs and truly cyber-resilient HMG implementations.

In future research, the SWO–FOPI(1 + PDN) framework [140], can be extended toward robust control classifications for hybrid AC/DC microgrids. By integrating robust schemes such as sliding mode, or adaptive backstepping into the optimization-based controller design, the system can achieve higher resilience against uncertainties, parameter variations, and renewable intermittency. Also, the adaptive power-sharing strategy [141] can be extended within a robust control framework for HMGs. This may involve designing algorithms that, in addition to considering the state of charge and instantaneous power of storage systems, are also capable of handling uncertainties, severe load variations, and communication disturbances.

In addition, combining risk assessment models with robust control design can help identify system vulnerabilities and prevent instability [142]. Also, combining data-driven probabilistic predictions with robust control, especially in the field of energy planning and distributed resource management, can provide an effective solution to reduce uncertainty and optimize operation in future MGs [143]. Also, given the limited attention given to backstepping control methods in HMG applications, future research can focus on developing an advanced adaptive backstepping controller specifically designed to address the unique challenges of HMGs. This controller should simultaneously handle parametric uncertainties, disturbances from intermittent RESs, and operational mode transitions. Also, the need for standardized evaluation frameworks for AI/ML-based robust controllers in hybrid AC/DC microgrids. In particular, development of public benchmark scenarios and open datasets covering diverse MG operating conditions is recommended, to enable fair comparison of control approaches. HIL testbeds and real-time co-simulation platforms should be established to validate controller performance under realistic grid conditions recommendations. Formal certification methodologies, including rigorous stability and safety proofs (via Lyapunov or barrier certificates) and compliance with cyber-physical security standards, should be developed. Such practices would foster trust in AI controllers by providing verifiable performance and safety guarantees and could help align research and industry through shared benchmarks.

Also, one of the key challenges in decentralized control of hybrid AC/DC microgrids is system scalability. As the number of distributed power resources and interconnected MGs increases, computational burden and communication requirements also grow. Therefore, distributed control architectures must ensure sufficient redundancy and modularity. For instance, a scalable clustering architecture has been proposed that employs Energy Networking Units to provide a reconfigurable topology for flexible interconnection of AC and DC sub-grids [144].

Furthermore, the development of communication efficient algorithms such as event-triggered or distributed optimization methods can reduce the need for continuous data exchange and thus minimize communication overhead. Also, AI- and ML- based controllers equipped with formal stability guarantees (safe learning with barrier functions or reachability analysis) can provide novel pathways for scalable and adaptive control. In summary, these combined approaches ranging from clustering and hierarchical architectures to communication-efficient algorithms and intelligent edge-based control hold strong potential to significantly enhance the scalability and efficiency of decentralized control strategies for hybrid AC/DC microgrids in future research.

Additionally, bridging the gap between theoretical development and experimental verification is imperative to advance the field from conceptual frameworks to deployable solutions for enhancing the resilience and reliability of modern power systems.

Finally, looking ahead, the anticipated development of 6G networks promises to revolutionize MG operations further. Future 6G networks are expected to provide sub-millisecond ultra-reliable low-latency communication, massive machine-type connectivity, and native edge intelligence. These features create a unified communication–computation platform that can support real-time distributed control and scalable multi-agent coordination across a large hybrid AC/DC microgrid. For example, 6G’s URLLC and high-bandwidth links could enable predictive dispatch of distributed generators and real-time HIL optimization, by allowing control and monitoring signals to be exchanged with minimal delay [145]. The integrated edge-AI capabilities of 5G and 6G would allow advanced AI/ML algorithms, such as multi-agent reinforcement learning or distributed model-predictive control, to be implemented throughout the grid for intelligent energy management and rapid fault response. Collectively, these developments could greatly enhance the scalability, intelligence, and resilience of future HMG operations.

7. Conclusions

Several robust control techniques for AC/DC HMGs were examined in this research. The study review’s findings demonstrate the need of robust control in enhancing stability, controlling uncertainties, and enhancing performance from RESs. The use of techniques including intelligent algorithms, SMC, and $H_{\infty }$ control has improved voltage and frequency management, increased fault tolerance, and expanded system flexibility. Additionally, it was discovered that distributed and hierarchical control structures work extremely well for better satisfying the intricate requirements of HMGs. Given the current challenges, including the growing penetration of intermittent resources, emerging grid instabilities, and the increasing demand for real-time responsiveness, the development of innovative and resilient control techniques is not only a vital pathway but also an unavoidable necessity for the future evolution of smart MGs. Future directions include the integration of advanced machine learning and AI techniques, the standardization and refinement of hierarchical and distributed control frameworks, and the seamless coupling of real-time computational technologies with HIL platforms. Such advancements hold the potential to enhance the stability and reliability of MGs while paving the way toward sustainable, flexible, and scalable energy systems.