Advanced MPPT analysis of PV systems: A comparative study of robust nonlinear controllers, model-free controllers, and machine learning techniques

Achieving maximum power point tracking in photovoltaic systems is critical for maximizing energy conversion efficiency under varying environmental conditions. This paper proposes a novel hybrid MPPT strategy that addresses critical limitations of existing methods by integrating the advanced nonlinear controllers mainly the adaptive barrier condition super-twisting sliding mode controller and synergetic super-twisting sliding mode controller are investigated for their superior disturbance rejection and convergence speed. A novel reference generation mechanism utilizing a bi-directional recurrent neural network is integrated to provide precise and adaptive reference trajectories to overcome challenges related to nonlinearity and time-varying system behavior and enhancing controller precision. The proposed control strategies are rigorously tested through simulations conducted in MATLAB/Simulink and further validated using real-time controller hardware-in-the-loop experiments with practical PV system data. Comparative analysis is performed against AI-based machine learning techniques, including reinforcement learning method, under dynamic irradiance and temperature profiles. Key performance metrics including tracking accuracy, steady-state error, convergence time, and robustness demonstrate that the proposed nonlinear-AI hybrid method significantly outperforms conventional MPPT approaches. Results indicate that the nonlinear controllers, particularly adaptive barrier condition supertwisting sliding mode controller demonstrate superior performance in terms of robustness and steady-state accuracy in both simulation and real-time environments The proposed design effectively mitigates oscillation, chattering, and instability, bridging simulation and real-world applicability. The hardware in loop experimental validation underscores the practical viability of these methods, highlighting the trade-offs between nonlinear robustness and AI-driven adaptability. This study bridges the gap between simulation and real-world applications, offering insights into the design and implementation of advanced MPPT strategies. The findings support the development of hybrid control frameworks, combining the strengths of robust nonlinear controllers and AI-based techniques, for next-generation renewable energy systems.