AI–Lagrangian MPPT: A New Paradigm for Explainable and High-Performance Photovoltaic Optimization

The major challenge in photovoltaic (PV) maximum power point tracking (MPPT) systems is finding a balance between the high performance of artificial intelligence techniques and the interpretability and reliability of physics-based approaches. This paper proposes a new MPPT controller based on a combination of Lagrangian and artificial intelligence techniques. In the proposed method, power maximization is modeled as a Lagrangian system, and the duty cycle is determined by physics equations. An artificial neural network is utilized to adaptively adjust the parameters of inertia and damping in real-time based on an eight-dimensional feature vector. Simulation results for step changes, ramp changes, and partial shading conditions confirm the effectiveness of the approach. The controller has 99.7% tracking efficiency in 18.2 ms, which is superior to P&O (55 ms), INC (45 ms), PSO (28.3 ms), and conventional ANN (22.5 ms). Under partial shading conditions, the controller correctly identifies the global maximum power point. The steady-state ripple is very low (±0.1 W), and the transient energy losses are significantly reduced compared to the benchmark algorithms. The results confirm that the integration of Lagrangian dynamics with adaptive neural tuning provides a systematic and efficient approach for designing reliable PV energy systems, effectively bridging the gap between data-driven and physics-based methods.