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Summary In order to achieve current sharing and bus voltage stability in distributed resources (DR) if a conventional drop control is used, a trade‐off must always be considered. When the bus voltage is restored to the nominal level, the desired current sharing is sacrificed. This paper proposes an adaptive and robust droop controller to achieve the goals of current sharing and bus voltage stability. This droop controller is a model reference adaptive control (MRAC) in which the second‐order reference transfer function is derived from the transfer function showing the dynamical behavior of DRs. In the proposed adaptive droop controller, the Lyapunov stability theory is used to extract the adaption law. The suggested adaptive droop controller is implemented in the secondary control layer and modifies the droop resistance adaptively. The effectiveness of the proposed method is validated by the experimental and simulation results.

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This study suggests an intelligent control method for achieving the maximum power point tracking (MPPT) in photovoltaic (PV) systems. MPPT technologies in PV systems are used to transfer maximum power under various environmental conditions. To improve the performance of the MPPT, the study develops a two‐level adaptive control structure that can facilitate system control and efficiently handle uncertainties and perturbations in the PV systems and in the environment. The first control level is a ripple correlation control (RCC), and the second is a model reference adaptive control (MRAC). The paper emphasizes mainly on designing an MRAC algorithm that improves the underdamped dynamic response of the PV system. The original state‐space equation of the PV system is time‐varying and nonlinear, and its step response contains oscillatory transients that damp slowly. Using a self‐constructing Lyapunov neural network (SCLNN), an adaptive law of the controller is derived for the MRAC system to remove the underdamped modes in the PV systems. Also, a self‐constructing mechanism for generating blocks in the recursive unit of the SCLNN is introduced. Since the size of the SCLNN is optimal and minimal, the computation time, which is an important factor for real‐time implementation, is greatly reduced. It is shown that the proposed control algorithm enables the system to converge to the maximum power point in milliseconds.