摘要： Energy production capability of a photovoltaic (PV) system is extensively depends upon the ambient temperature (T) and solar irradiance (Ee). In order to adapt the ever increasing interest in energy, the PV array must be operated at the maximum power point (MPP). However, due to varying climatic conditions, there is a low energy ef?ciency problem. In this research article, a robust and ef?cient nonlinear sliding mode control (SMC) based maximum power point tracking (MPPT) technique is designed to extract maximum power from the PV array. This study uses arti?cial feed-forward neural network (AFNN) to generate the reference voltage for MPPT using non-inverting DC-DC Buck-Boost converter. Asymptotically convergence is ensures using Lyapunov stability criteria. The MATLAB/SIMULINK platform is used to design, simulate and test the performance of the proposed technique. To further validate the proposed control technique in terms of ef?ciency, tracking speed and robustness, results are compared with the non-linear backstepping (B) technique under continuous conditions of environment, faults and parametric uncertainties.