The coronavirus disease (COVID‐19) pandemic has impacted many nations around the world. Recently, new variant of this virus has been identified that have a much higher rate of transmission. Although vaccine production and distribution are currently underway, non‐pharmacological interventions are still being implemented as an important and fundamental strategy to control the spread of the virus in countries around the world. To realize and forecast the transmission dynamics of this disease, mathematical models can be very effective. Various mathematical modeling methods have been proposed to investigate the transmission patterns of this new infection. In this paper, we utilized the fractional‐order dynamics of COVID‐19. The goal is to control the prevalence of the disease using non‐pharmacological interventions. In this paper, a novel fractional‐order backstepping sliding mode control (FOBSMC) is proposed for non‐pharmacological decisions. Recently, new variant of this virus have been identified that have a much higher rate of transmission, so finally the effectiveness of the proposed controller in the presence of new variant of COVID‐19 is investigated.

Wind energy systems are pollution free and clean form of the renewable energy production. The dynamic model of a wind turbine system based on a doubly fed induction generator (DFIG) is exposed to external disturbances, uncertainties, and nonlinear dynamics. In this paper to ensure the system robustness against external disturbance and uncertainty in system parameters, a novel optimized fractional order robust adaptive sliding mode controller is proposed by utilizing a disturbance observer. The controller's main goal is to track the maximum power point of the wind turbine. In order to show the superiority of the proposed method, the results under normal conditions and in the presence of disturbance and uncertainty have been compared with the classical sliding mode control (SMC) and adaptive sliding mode control (ASMC). The parameters of all three controllers have been optimized by ant colony optimization (ACO) algorithm. The proposed method does not need the knowledge of the upper bounds of model uncertainty and disturbance. Also by using the fractional order operators in the control signal of the proposed method, its robustness against model uncertainty and disturbance is increased and it can extract the maximum power than the other compared methods.

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