This paper presents an adaptive and intelligent power control approach for microgrid systems in the grid-connected operation mode. The proposed critic-based adaptive control system contains a neuro-fuzzy controller and a fuzzy critic agent. The fuzzy critic agent employs a reinforcement learning algorithm based on neuro-dynamic programming. The system feedback is made available to the critic agent’s input as the controller’s action in the previous state.
The evaluation or reinforcement signal produced by the critic agent together with the back-propagation of error is then used for online tuning of the output layer weights of the neuro-fuzzy controller. The proposed controller shows superior results compared with the traditional PI control. The transient response time is significantly reduced, poweroscillations are eliminated, and fast convergence is achieved. The simple design and improved dynamic behavior of the proposed controller make it a promising nominee for power control of microgrid systems.