# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 1 Introduction  
Currently, the social economy and industrial production are developing rapidly, incurring an increasing amount of energy consumption. Nevertheless, fossil energy supplies are limited, such as oil, gas, and coal, the use of which can pollute the environment. In this manner, renewable sources of electricity, like solar energy, have been destined for a brighter future. For example, photovoltaic (PV) power-generation technology can effectively relieve the pressure of traditional thermal power generation; therefore, it has attracted significant attention recently [1; 2; 3]. As a bridge between circuits and the power grid, grid-connected inverters (GCIs) are an important component to realize the stability, reliability, and high efficiency of power-generation control systems [4; 5]. There are two main conventional control strategies of GCI: pulse-width modulation and hysteresis-controlled pulse modulation [6; 7]. The problems that are encountered by the two strategies are that the switching of frequency fails to maintain stability, and the total harmonic distortion is high. As the performance of digital processors improves, model predictive control (MPC) has been introduced to the control technology for grid-connected inverters [8; 9; 10; 11]. MPC can omit the current inner loop control and pulse width modulation, yielding effortless application and strong adaptability. Specifically, [PERSON]. [9] designed an MPC method based on the power prediction model, where the applied voltage vector (VV) was chosen by minimizing the power ripple. Therefore, flexible power regulation can be achieved. In addition to aiming for predictive power, predictive current control is another feasible technical route. [PERSON]. [10] forecasts the possible behavior of the future grid current in accordance with the GCI model, namely model predictive current control(MPCC). Thereafter, the optimal VV can be selected so that the current error between the reference value and the predicted value is minimized in the cost function. It is evident that the existing MPC strategies depend on accurate models of the grid-connected systems [12]. In practice, some uncertainties are inevitable in terms of inverter component parameters, considering the variations in climatic conditions. Therefore, satisfactory control performance can no longer be assured under model mismatches. So far, many researchers have posed a concern for the robustness of existing strategies [13; 14; 15]. On one hand, a variety of observers have been introduced in MPC to improve its performance and robustness. Particularly, disturbance observers are the principal force for this improvement due to their undisturbed grid current. In [13], a disturbance observer was built to estimate the actual uncertain disturbances in real time, and its output was employed to design a sliding mode controller so that the output voltage of the GCI could be controlled. In this manner, the robustness of the system was effectively improved, owing to the adaptively compensated disturbances. Similarly, [PERSON]. [14] conceived of an extended Kalman filter (EKF) as the disturbance observer for estimating the grid voltage. Even though the grid voltages were seriously distorted, the current could still be controlled with a fast dynamic response, as expected. It should be noted that most observers are still determined by inverter mathematical models. In other words, these strategies fail to remove the influence of parameter mismatch. Furthermore, they require more effort for their tuning work. Gradually, data-driven control has come to the attention of the PV system control [16; 17]. These models introduce anomalous data at predetermined intensities and time slots, while the risk nature of false data injection fails to be reflected in real time. Additionally, a model-free predictive control based on the current gradient update was studied in [18], where no system parameters are required.