###### Abstract
Thus, the high gain at the fundamental frequency and harmonic frequency, as well as the zero steady-state tracking error were eliminated. However, the sensitivity of the disturbance to power quality and the uncertainties of the filter parameters were not considered. The authors of [15], proposed an adaptive control scheme that used a highly accurate track with existing inherent resonance and voltage harmonic distortion and gave the power grid a better, more robust performance. Smart microgrid systems need to consider more dynamic, complex external interference and uncertain input factors to be able to respond to the needs of practical engineering. Therefore, the improvement of power quality under nominal conditions has limitations. For example, the load frequency quadratic control method in [16] that is based on an unknown stochastic input observer and linear quadratic regulator (LQR), not only effectively solved the uncertainties, such as load variation and measurement noise, but also produced better robustness. Although the research on filter-based powers system is convenient for solving a lot of problems, the filter is attached with complex conjugate poles and resonance damping requirements, which makes the power system very sensitive to parameter uncertainty and the design of its control strategy more complex. In [17], the authors proposed a virtual inductance method to improve the stability of the microgrid under a constant power load. However, whether the control strategy can be extended to the AC/DC hybrid microgrid was not considered. The work of [18] considered distributed quadratic consistency and fault-tolerant control to compensate for the frequency and voltage errors and to achieve accurate power distribution. However, the influence of the involved load change or fault was ignored. The author [19] designed an efficient and reliable intelligent control strategy for the secondary reconfigurable inverter for the off grid microgrid and diagnosed the fault. In [2], the authors proposed a robust nested loop control scheme, with different load and filter parameter uncertainties that enabled two types of modes to operate stably; however, this required an accurate mathematical model. In [20], the authors designed a centralized robust controller through a suboptimal solution of a convex optimization problem to resolve the influence of parameter uncertainty and unknown disturbances. However, there was no bounded analysis of parameters, and the time delay was not considered. In [21], a new dynamic sliding mode control (DSMC) solved the shortcomings of traditional control state references and sliding coefficient calculation. This compensated for the impact of time delay and uncertainty in the system, so the system had robustness and stability. However, the authors did not consider the capacitor voltage variation of the load itself. The authors of [22], based on droop control, designed a sliding mode control method to overcome the difference caused by feedback line impedance, reduce the error of voltage and reactive power, and improve the accuracy of data adjustment. In [23], the authors proposed that the stabilization algorithm was bound to deal with a class of boundless uncertainties, but the algorithm was too simple and not applicable to various types of uncertainties. In [24; 25], robust state feedback control and steady-state robust state estimation for uncertain linear systems were designed by the authors to refine the types of uncertainties and to achieve better cost estimates, but the non-linear systems were not considered. The work of [26], a grid connected inverter control technology based on dq transformation, was introduced, which reduced the cost and the change of power harmonics, ensured the power quality, and made the power output stable; but it did not consider the impacts of the LCL filter and inverter structures on the system oscillation and output distortion.