This paper proposed a novel adaptive robust backstepping control scheme for DC-DC buck converter subjected to external disturbance and system uncertainty. Uncertainty in the load resistance and the input voltage represent the big challenge in buck converter control. In this work, an adaptive estimator for matched and mismatched uncertainties based backstepping control is applied for DC-DC buck converter. The updating laws are determined based on the lyapunov theorem. Thus, the difference between the estimated parameters and actual parameters converges to zero. The proposed control method is compared with the conventional sliding mode control and integral sliding mode control. Simulation results demonstrate the effectiveness and robustness of the proposed controller.

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The photovoltaic system considers one of the important renewable sources of energy that using solar cell. Limitation and crisis of other energy resource make the photovoltaic system is growing. This motivated the researchers to improve and maximize renewable energy sources effectiveness. The optimal operating point is located in the PV voltage curve of the solar cell which is called Maximum Power Point (MPP). This point changes in nonlinear form with varying of solar incandescence, temperature and solar cell properties. Recently more methods are developed to get optimal value of MPPT and one of these methods is the Extremum-Seeking Control (ESC) which is based on filter operation. In this paper different optimization methods are presented such as Genetic Algorithm (GA), Grey Wof Optimizer (GWO) and Ant Lion Optimizer (ALO). They are used as a tuning tool for ESC controller parameters to improve the MPPT performance. All methods are tested in the MATLAB environment, the result shows that the effectiveness of swarm optimization, especially GWO in term the efficiency and speed of converging.

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