Deep reinforcement learning tuned type-3 fuzzy PID controller: AC microgrid case study

The article introduces a groundbreaking approach to enhance the performance of fuzzy PID controllers in AC microgrid systems by employing deep reinforcement learning. The proposed type-3 fuzzy PID controller, equipped with deep deterministic policy gradient (DDPG) RL, demonstrates exceptional adaptability to system uncertainties and nonlinearities. The study highlights the superiority of the RL-tuned controller over traditional methods, showcasing its ability to handle high levels of uncertainty and measurement noise more effectively. The article presents extensive simulation results, comparing the proposed controller with other state-of-the-art methods, and demonstrates its robust performance in various operational conditions. The innovative combination of type-3 fuzzy logic and DDPG RL sets a new standard for control system design, offering a promising solution for real-time frequency regulation in AC microgrids.