<![CDATA[Microgrids (MG) are complex systems that integrate distributed energy resources to provide reliable and efficient power to local loads. Due to the dynamic and uncertain nature of the MG environment, intelligent control techniques have become a popular solution to ensure optimal performance. This paper provides an overview of the recent advances in intelligent control techniques applied in MG, including neural networks, model predictive control, game theory, deep reinforcement learning, and Bayesian controllers. The paper also presents a discussion of the advantages and limitations of these techniques, highlighting the challenges associated with implementing them in MG systems. Finally, investigation of the existing literature on the performance of intelligent control techniques in MG systems is presented, providing insights into their effectiveness in improving the energy efficiency, stability, and reliability of MG systems.]]>
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