Adaptive Model Predictive Control for LCL-Filter Grid-Tied Inverters

This article proposes adaptive continuous-control-set model predictive control (CCS-MPC) for three-phase LCL-filter grid-tied inverters in the stationary frame. The proposed control provides stability against the LCL-filter resonance and removes the need for active damping or passive damping. Furthermore, it can track sinusoidal references in the stationary frame close to zero offsets. Inductance variation of a grid is a challenging issue, which can shift the LCL-filter resonance. Even though CCS-MPC generally has good stability for parameter mismatches, the control is optimized for the nominal value of the parameters. A relatively large variation may deteriorate the performance of the control and cause instability in the closed-loop system. To solve this problem, the concept of CCS-MPC is developed to work with a variety of the grid-inductance changes. The controller coefficients are chosen with a sensitivity analysis to provide enough gain margins and phase margins for the whole interval of the presumption grid-inductance variation. Also, this selection guarantees the control robustness against mismatches of the LCL-filter capacitance and inductance. The real-time computation of advanced controllers is a challenging issue. The computation burden of the proposed CCS-MPC is mostly performed offline and just some simple numerical expression is executed in real time. Thus, it does not require a powerful but expensive processor for implementation. The simulation and experimental results prove the performance of the proposed controller for handling the LCL-filter resonance, grid-inductance variations, and tracking sinusoidal references.