Microgrids (MGs) are emerging as a prominent, sustainable, and efficient alternative to conventional power systems. However, the design of their controllers remains challenging, especially when addressing Power Quality (PQ) disturbances. This study proposes using full-state feedback, specifically via a Linear Quadratic Regulator, to enhance the robustness and stability of Proportional-Integral and Proportional-Resonant controllers. The controller parameters are optimized by minimizing key performance indices, including settling time, overshoot, and steady-state error, using three widely adopted metaheuristic algorithms: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO). The relative effectiveness of these algorithms is rigorously assessed through convergence behavior and exploration¿exploitation analysis to identify the most suitable solver for MG controller tuning. Furthermore, the study demonstrates how individually optimized control actions can be integrated into a unified cooperative strategy, enabling coordinated operation among controllers. Both individual and cooperative control schemes are evaluated across 15 realistic PQ scenarios, which include current imbalance, harmonic distortion, and their combination. Results indicate that GWO achieves superior tuning precision and that the proposed cooperative controllers consistently outperform their standalone counterparts in PQ indices. These findings highlight the relevance of metaheuristic optimization for controller design, demonstrating improved disturbance rejection and practical feasibility for modern MG applications.
