Energy production from renewable sources offers an efficient alternative non-polluting and sustainable solution. Among renewable energies

solar energy represents the most important source

the most efficient and the least expensive compared to other renewable sources. Electric power generation systems from the sun's energy typically characterized by their low efficiency. However

it is known that photovoltaic pumping systems are the most economical solution especially in rural areas. This work deals with the modeling and the vector control of a solar photovoltaic (PV) pumping system. The main objective of this study is to improve optimization techniques that maximize the overall efficiency of the pumping system. In order to optimize their energy efficiency whatever

the weather conditions

we inserted between the inverter and the photovoltaic generator (GPV) a maximum power point adapter known as Maximum Power Point Tracking (MPPT). Among the various MPPT techniques presented in the literature

we adopted the adaptive neuro-fuzzy controller (ANFIS). In addition

the performance of the sliding vector control associated with the neural network was developed and evaluated. Finally

simulation work under Matlab / Simulink was achieved to examine the performance of a photovoltaic conversion chain intended for pumping and to verify the effectiveness of the speed control under various instructions applied to the system. According to the study

we have done on the improvement of sliding mode control with neural network. Note that the sliding-neuron control provides better results compared to other techniques in terms of improved chattering phenomenon and less deviation from its reference.