## 4 Review of Microgrid Control and Design
### Conventional and Advanced Control Techniques in Microgrids
#### 4.6.5 AI-Based Control Techniques  
A wide variety of heuristic algorithms have been utilized in the literature to improve MG's energy management and optimization. These include methodologies such as particle swarm optimization, fuzzy logic, neural networks, and genetic algorithms. The successful application of these techniques extends to both the connected-to-the-grid and standalone modes of MGs. Additionally, the literature introduces robust methodologies aimed at finely tuning controller parameters. Among them are the differential evolution algorithm [120], the bacterial foraging optimization technique [121], the colonial competitive algorithm [122], and the cuckoo search technique [123]. Below is a quick analysis of particle swarm optimization, fuzzy logic, and neural network optimization. Particle Swarm Optimization (PSO)  
The PSO technique has garnered significant research efforts due to its effectiveness in optimizing uncertain parameters for a wide range of complex optimization problems. In ref [124], the authors developed an intelligent online technique combining the PSO algorithm and fuzzy logic to optimize PI gains. The proposed technique achieves better stability performance compared to fuzzy logic and Ziegler-Nichols and is robust against environmental and dynamic variations. Another work is presented in [125] by [PERSON] et al., using the PSO technique to optimally design the microgrid in every operation mode. It is employed to look for the best power sharing coefficient, filter, and PI controller parameter values. Fuzzy Logic Control (FL)  
The FL control technique is extensively used in various domains. It is considered one of the most significant intelligent tools for resolving power optimization problems related to renewable energy sources. In such a scenario, this technique is often used to optimize energy resources such as wind and solar power. In islanded mode, the authors in [126]suggested three methods for smoothing wind power. Fuzzy logic is used to construct a pitch angle controller that helps to partially smooth changes in output wind power. Another application of fuzzy logic is introduced in [127], where an automatic adjustment structure of PI regulator parameters is used for active and reactive power control in MGs. The concept of neural networks is fundamentally inspired by the workings of the human brain, and these networks have found applications across various research domains. Neural networks prove to be highly effective in tasks such as system parameter identification, control, and optimization, whether applied in offline or real-time scenarios. In the research conducted by [PERSON] et al. [128], they utilized neural network identification and current regulation to devise an adaptive interfacing scheme for MGs functioning in a grid-connected mode. Additionally, another study by the authors in [129] introduced an intelligent online management approach for active and reactive power in a synchronous static compensator. This method, driven by neural networks, aims to prevent instability and failures in autonomous MG systems. Adaptive control techniques can be used to successfully achieve robust convergence, stability, and dynamic system tracking. Adaptive solutions are a desirable way to address inadequate controller performance when a system's operating conditions change. A number of studies, including adaptive PI/PID control [130], adaptive droop control [131], adaptive sliding mode control [132], and reinforcement learning [133], have been reported in the literature on adaptive control in MGs. Numerous academics are interested in the application of the robust H-infinity control concept to microgrid frequency and voltage regulation for a number of reasons. Firstly, H-infinity techniques and \(\mu\)-synthesis can address control objectives such as disturbance attenuation, robust stabilization of uncertain systems, and shaping the open-loop response [134]. The founded solution can be considered optimal concerning the defined criterion.