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A high-performance control system is essential to transfer maximum power from wind power generation system (WPGS) to the utility grid. In this paper, a fuzzy fractional-order terminal sliding mode control (Fuzzy-FOSMC) is presented based on the boundary layer approach. This boundary layer approach leads to the trade-off between chattering elimination and control performances. Initially a fractional order terminal sliding mode control (FOTSMC) is designed in this paper. Then, the reaching control part of the FOTSMC is replaced by a fuzzy system that eliminates the chattering even in the presence of lumped parametric uncertainties. The fuzzy control part is designed such that:(a) it maintains the stability of the system by introducing a non-linear slope inside the thin boundary layer near the sliding surface, and (b) it eliminates the chattering by acting like a saturation function. A novel wind speed estimation technique is also proposed in this paper based on Gaussian process regression (GPR). The inputs to the GPR framework are selected as the wind turbine power and its rotational speed. The superior performance of the proposed wind speed estimation technique is verified using error comparison with pre-existing techniques. The stability of the proposed GPR-based Fuzzy-FOSMC control paradigm is ensured by using the Lyapunov stability theorem. The proposed paradigm is compared with benchmark sliding mode control (SMC) and FOTSMC strategies. The proposed Fuzzy-FOSMC performance in terms of chattering elimination and stability is validated under normal conditions and lumped parametric uncertainties using extensive simulations in Matlab/SIMULINK and processor in the loop based experimental workbench., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

PV (Photovoltaic) cells have nonlinear current-voltage (I - V) and power-voltage (P - V) characteristics with a distinct maximum power point (MPP) that entirely depends on the ambient meteorological conditions (i.e. solar irradiance and temperature). Hence, to continuously extract and deliver the maximum possible power from the PV system, under given meteorological conditions, the maximum power point tracking (MPPT) control strategy needs to be formulated that continuously operates the PV system at its MPP. To achieve this goal, a hybrid nonlinear, very fast and efficient MPPT control strategy, based on the robust integral backstepping (RIB) control, is formulated in this research article. The simulation testbed comprises a standalone PV array, a non-inverting buck-boost (NIBB) DC-DC power converter, a purely resistive and a dynamic load (sound system). The proposed MPPT control scheme consists of two loops, where the first loop generates the real-time offline reference peak power voltage through an adaptive neuro-fuzzy inference system (ANFIS) network, which is then utilized in the second loop as a set-point value for generating a control signal and then forcing the PV system to be operated at this set-point by continuously adjusting the duty ratio of the power converter. This control strategy exhibits no overshoot, fast convergence, good transient response, fast rising and settling times and minimum output tracking error. The MATLAB/Simulink platform is used to test the performance of the proposed MPPT strategy against varying meteorological conditions, plant current and voltage faults and plant parametric uncertainties. To validate the superiority of the proposed control strategy, a comparative analysis of the proposed control strategy is presented with the nonlinear backstepping (B), integral backstepping controller (IB) and conventional PID and P&O based MPPT controllers., Competing Interests: The authors have declared that no competing interests exist.

The state-of-charge (SoC) of an energy storage system (ESS) should be kept in a certain safe range for ensuring its state-of-health (SoH) as well as higher efficiency. This procedure maximizes the power capacity of the ESSs all the times. Furthermore, economic load dispatch (ELD) is implemented to allocate power among various ESSs, with the aim of fully meeting the load demand and reducing the total operating cost. In this research article, a distributed multi-agent consensus based control algorithm is proposed for multiple battery energy storage systems (BESSs), operating in a microgrid (MG), for fulfilling several objectives, including: SoC trajectories tracking control, economic load dispatch, active and reactive power sharing control, and voltage and frequency regulation (using the leader-follower consensus approach). The proposed algorithm considers the hierarchical control structure of the BESSs and the frequency/voltage droop controllers with limited information exchange among the BESSs. It embodies both self and communication time-delays, and achieves its objectives along with offering plug-and-play capability and robustness against communication link failure. Matlab/Simulink platform is used to test and validate the performance of the proposed algorithm under load disturbances through extensive simulations carried out on a modified IEEE 57-bus system. A detailed comparative analysis of the proposed distributed control strategy is carried out with the distributed PI-based conventional control strategy for demonstrating its superior performance., Competing Interests: The authors have declared that no competing interests exist.

Congenital Adrenal Hyperplasia (CAH) is caused by congenital insufficiency of the enzyme 21 - hydroxylase (21-OHD) in the cortisol synthesis pathway. Because of the virilizing effects of androgens over-production, affected girls develop clitoral hypertrophy. Three patients with CAH are discussed below along with their surgical management and follow-up.