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This paper presents a novel hybrid technique for tracking the maximum power point of the photovoltaic panel. This approach includes two loops: The first one is the ANN (Artificial neural network) loop that is used to estimate and generate the reference of optimal voltage. While, the second loop consists of the BSM (Backstepping Sliding Mode) controller. Effectively, the proposed controller is designed to track the signal of the desired voltage, which is generated using the first loop, by adjusting the duty cycle of the boost converter. Thus, this loop allows the DC/DC converter to product the maximum power at the terminals of the PV module and the load. Indeed, in contrary to the traditional backstepping, the proposed ANN-BSM technique guarantee zero steady-state error. On the one hand, by using the ANN, the system can quickly predict the desired optimal voltage, also it allows the system to avoid the unnecessary calculations and research of the maximum point of power. On the other hand, the sliding mode and the backstepping controller are used to provide the good performances and robustness against the rapid changes of insolation. In addition, the asymptotic stability of this system is made by applying the Lyapunov approach. To show the effectiveness and the tracking performances of the proposed ANN-BSM technique, a comparative study with the classical methods, P&O and incremental conductance algorithms, and hybrid approaches such as the ANN-Backstepping controllers and the ANN-Integral Sliding mode, is investigated in MATLAB/Simulink software.

MPPT Design Using Artificial Neural Network and Backstepping Sliding Mode Approach for Photovoltaic System under Various Weather Conditions