Robust model predictive control of a: DC–DC boost converter for grid-connected photovoltaic systems using continuous-time and Lyapunov-based approaches
Abstract
The present paper proposes a robust continuous-time model predictive control (CTMPC) strategy for enhancing the performance of grid-connected photovoltaic (PV) systems operating under uncertainties and varying environmental conditions. The primary contribution of this work lies in the integration of a nonlinear extended state observer (NESO) with CTMPC to estimate and compensate for external disturbances and model uncertainties in real time. A precise PV model has been developed, and a Lyapunov-based stability framework has been employed to guarantee closed-loop stability and improve dynamic response. The proposed control approach is implemented on a DC–DC boost converter to ensure effective power management and reliable operation. The simulation results demonstrate that the proposed NESO-CTMPC scheme exhibits superior dynamic response, enhanced disturbance rejection, augmented tracking performance, and elevated robustness in comparison with conventional control methods. These results substantiate the efficacy of the proposed strategy in enhancing the efficiency, stability, and adaptability of grid-connected PV systems.