Comprehensive control strategy for standalone photovoltaic systems with integrated optimum power harvesting and voltage regulation through microcontroller in the loop experimentation.

This paper introduces a dual-objective control framework for standalone photovoltaic (PV) systems that uniquely integrates maximum power point tracking (MPPT) with precise DC load voltage regulation. Unlike existing approaches that concentrate almost exclusively on power optimization, the proposed system simultaneously ensures both efficient energy harvesting and robust output voltage stability under fluctuating climatic and load conditions. The novelty lies in the design of a reference voltage estimator (RVE)-a sensorless MPPT mechanism that fuses explicit irradiance estimation with a radial basis function neural network-combined with two Lyapunov-based nonlinear controllers supervising a two-stage Boost-Buck converter architecture. This coordinated design enables accurate real-time MPP prediction, finite-time load-side voltage stabilization, and decoupled handling of PV-side and load-side dynamics. The system is implemented through microcontroller-in-the-loop experimentation and validated under diverse and extreme disturbances. Results demonstrate exceptionally low mean absolute errors (0.1253 V for MPPT and 0.0793 V for load regulation) with rapid recovery times (< 3 ms), confirming superior efficiency, reliability, and resilience. This work bridges a critical research gap by experimentally proving that stable voltage regulation can be unified with optimal power harvesting in a single architecture, offering a deployable solution for standalone PV systems in real-world conditions.