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In this paper, by combining fractional calculus and sliding mode control theory, a new fractional order adaptive terminal sliding mode controller is proposed for the maximum power point tracking in a solar cell. To find the maximum power point, the incremental conductance method has been used. First, a fractional order terminal sliding mode controller is designed in which the control law depends on knowing the upper bound of uncertainty in the system, but in practical application it is difficult or in some cases impossible to calculate this upper limit. In this paper, an adaptive law is given for online calculating of this parameter. The stability proof of the sliding surface, as well as the proof of finite time convergence of closed-loop system, are investigated using the Lyapunov theory. Finally, the performance of the proposed controller is evaluated both in normal and partial shading conditions. For a better comparison of the proposed controller, the performance of this controller is compared in the presence of load variations and the variations of system parameters with the conventional (integer order) terminal sliding mode control.

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Since the introduction of the first silicon solar cell, there have been steady improvements in its performance parameters such as light trapping, solar absorption, cell efficiency and manufacturing costs. In thin silicon cells, some of the light photons that are not absorbed by the semiconductor are always lose in various ways. The diffraction grating causes the photons to travel a longer light path due to the collision with this structure, which increases the length of the light path of the photons and cell absorption, that thus improving cell efficiency. In each of the mentioned structures, optimal materials and geometric properties have been used to achieve maximum efficiency of silicon cells. Intelligent optimization methods have been used to find the optimal geometric parameters for the structure. In choosing search methods from the two algorithms particle swarm optimization and genetics and creating a combination of the both, the positive feature of both algorithms was used to achieve the best answer. This combination has produced very positive results, which thereby, 23.293 efficiencies and 35.41 mA/cm2 short circuit current were obtained.