In this paper, a novel adaptive fractional-order fuzzy control method is developed for frequency control in
an ac microgrid (MG). A sequential general type-2 fuzzy system based on the radial basis neural network
is presented for online modeling of the frequency response of the MG. Then, the parameters of the type-2
fuzzy controller based on the online estimated model are online tuned, such that the frequency deviation
is minimized. The consequent parameters, i.e., centers of membership functions (MFs), the values of �-cuts,
and the type-reduction parameters are optimized based on the proposed algorithm, which is inspired from
the particle swarm optimization and artificial bee colony algorithm (PSO-ABC). The simulation results and
comparison with other methods show that the proposed control scheme is effective, and results in a good
and robust performance in the presence of variation of solar radiation, wind speed, load disturbance, and
time-varying dynamics of the other units of MG. Moreover, the effectiveness of the proposed fuzzy system
and the learning algorithm are examined by using white noise as the control input, and it is shown that the
proposed identification scheme results in good performance even in the noisy environment.