The temperature and radiation changes lead to obvious fluctuations in photovoltaic panels ' output power. To optimally exploit the PV system, maximum power point tracking (MPPT) is needed. Various offline and online methods for monitoring the MPP have been implemented. In this work, a maximum power point tracking (MPPT) technique was created which is primarily based on backstepping integral sliding mode controller (BISMC) design. The control scheme incorporates two parts: the first is based on an artificial neural network that provides the reference voltage, that supplies the maximum power regardless the environmental factors, which is given to the proposed BISMC control that is responsible on regulation of the duty cycle of the DC-DC boost converter's PWM applied switch (Mosfet). This strategy offers very low tracking error, and chattering improvement in tracking the MPP of a PV system when the environmental disturbances occur. The added integral action is very important in the control closed loop because it removes the steady state error. The method is compared with the ANN-sliding mode, PSO-backstepping and P&O-backstepping controllers in order to demonstrate its efficiency. In this later, the P&O (perturb & observe) and the PSO (particle swarm optimization) algorithms serve to generate reference voltage, that corresponds to the MPP, while the backstepping controller tracks this reference voltage. To reduce the PV system cost, a high-gain observer is designed, it requires only the PV output voltage sensor which enables the unmeasured PV system state variables online. This allows the minimization of the number of sensors in practical case because they have many disadvantages. The use of these sensors can lead to bulky system. Moreover, they are expensive. The simulation study is carried out under Matlab/Simulink. The results of the suggested MPPT reveal outstanding dynamic response under rapid changes of irradiation and temperature. The suggested method is more accurate and has fast convergence time about 2.2 ms and 1ms depends on the meteorological condition changes and 98% of efficiency to track the maximum power point.