This paper describes an energy management strategy for a DC microgrid that utilizes a hybrid renewable energy system (HRES) composed of a photovoltaic (PV) module, a wind turbine based on a permanent magnetic synchronous generator (PMSG), and a battery energy storage system (BESS). The strategy is designed to provide a flexible and reliable system architecture that ensures continuous power supply to loads under all conditions. The control scheme is based on the generation of reference source currents and the management of power flux. To optimize the supply–demand balance and ensure optimal power sharing, the strategy employs artificial intelligence algorithms that use previous data, constantly updated forecasts (such as weather forecasts and local production data), and other factors to control all system components in an optimal manner. A double-input single-output DC–DC converter is used to extract the maximum power point tracking (MPPT) from each source. This allows the converter to still transfer power from one source to another even if one of the sources is unable to generate power. In this HRES configuration, all the sources are connected in parallel through the common DC–DC converter. The strategy also includes a long short-term memory (LSTM) network-based forecasting approach to predict the available energy production and the battery state of charge (SOC). The system is tested using Matlab/Simulink and validated experimentally in a laboratory setting.

In this work, a lower limb exoskeleton with uncertainties and external disturbances under constrained motion has been controlled by developing a model‐free adaptive optimal fractional‐ordersuper‐twisting sliding mode (AOFSTSM) controller. Contrary to popular existing modeling methods, the modeling of the lower limb exoskeleton has been accomplished in the case of contact with the ground. A fractional‐order operator and super‐twisting sliding mode control have been combined to achieve an excellent tracking performance, chatter‐free control inputs, and robustness to external disturbances and uncertainties. The controller structure is model‐independent which has been derived without needing to know the dynamics of the system. An adaptive control strategy has been adopted as an approximating method to evaluate the uncertain dynamics of the system without the necessity for knowing the prior knowledge of the upper bounds. A grey wolf optimization technique has been used to find the optimal values of controller parameters. The stability of the overall system has been investigated and derived from the Lyapunov stability criterion. To validate the effectiveness of our proposed controller structure, a series of comparative simulations have been conducted with optimal fractional‐order super‐twisting sliding mode (OFSTSM) controller and fractional‐order super‐twisting sliding mode (FSTSM) controller. The comparative simulations have been also carried out with other types of recently existing control methods and recently existing optimization algorithms. The results of numerical simulations indicate the superiority of the AOFSTSM controller over other types of recently existing optimization algorithms and controllers in terms of tracking error and robustness towards the uncertainties and external disturbances.

In this paper, first-order and second-order sliding mode controllers for underactuated manipulators are proposed. Sliding mode control (SMC) is considered as an effective tool in different studies for control systems. However, the associated chattering phenomenon degrades the system performance. To overcome this phenomenon and track a desired trajectory, a twisting, a super-twisting and a modified super-twisting algorithms are presented respectively. The stability analysis is performed using a Lyapunov function for the proposed controllers. Further, the four different controllers are compared with each other. As an illustration, an example of an inverted pendulum is considered. Simulation results are given to demonstrate the effectiveness of the proposed approaches.

Bioimpedance spectroscopy (BIS) is an advanced measurement method for providing information on impedance changes at several frequencies by injecting a low current into a device under test and analyzing the response voltage. Several methods have been elaborated for BIS measurement, calculating impedance with a gain phase detector (GPD), IQ demodulation, and fast Fourier transform (FFT). Although the measurement method has a big influence on the measurement system performance, a systematical comparative study has not been performed yet. In this paper, we compare them based on simulations and experimental studies. To maintain similar conditions in the implementation of all methods, we use the same signal generator followed by a voltage-controlled current source (VCCS) as a signal generator. For performance analysis, three DUTs have been designed to imitate the typical behavior of biological tissues. A laboratory impedance analyzer is used as a reference. The comparison addresses magnitude measurement accuracy, phase measurement accuracy, signal processing, hardware complexity, and power consumption. The result shows that the FFT-based system excels with high accuracy for amplitude and phase measurement while providing the lowest hardware complexity, and power consumption, but it needs a much higher software complexity.

Derived Hench bioactive glass (BaG) containing boron (B) is explored in this work as it plays an important role in bone development and regeneration. B was also found to enhance BaG dissociation. However, it is only possible to incorporate a limited amount of B. To increase the amount of B in BaG, bioactive borosilicate glasses (BaG-B_x) were fabricated based on the use of the solution-gelation process (sol-gel). In this work, a high B content (20 wt.%) in BaG, respecting the conditions of bioactivity and biodegradability required by Hench, was achieved for the first time. The capability of BaG-B_x to form an apatite phase was assessed in vitro by immersion in simulated body fluid (SBF). Then, the chemical structure and the morphological changes in the fabricated BaG-B_x (x = 0, 5, 10 and 20) were studied. The formation of hydroxyapatite (HAp) layer was observed with X-ray diffraction (XRD) and infrared (IR) spectroscopy. The presence of HAp layer was confirmed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Enhanced bioactivity and chemical stability of BaG-B_x were evaluated with an ion exchange study based on Inductively Coupled Plasma–Optical Emission Spectrometry (ICP-OES) and energy dispersive spectroscopy (EDS). Results indicate that by increasing the concentration of B in BaG-Bx, the crystallization rate and the quality of the newly formed HAp layer on BaG-B_x surfaces can be improved. The presence of B also leads to enhanced degradation of BaGs in SBF. Accordingly, BAG-B_x can be used for bone regeneration, especially in children, because of its faster degradation as compared to B-free glass.

In this context, Fuzzy Fractional Order Proportional Integral Derivative (FOPID-FLC) controllers are emerged as efficient approaches due to their flexibility and ability to handle nonlinearities and uncertainties. This paper proposes the use of a FOPID-FLC controller for a two-degree-of-freedom (2-DOF) lower limb exoskeleton. Our proposal is based on an enhanced control approach that combines fuzzy logic advantages and fractional calculus benefits. Contrary to popular existing methods, that use the FLC to tune the FOPID parameters, the FLC in this work is used to generate the system torque depending on patient morphology. Indeed, our fundamental contribution is to design and implement an enhanced FOPID-FLC that achieves an adequate optimal control based on system rules composed of optimal torques and input data. The fractional calculus is approximated using successive first order filters. Next, a multi-objective optimization is established for the tuning of each FOPID parameters. Finally, the FLC is used to adjust the torque depending on the kid's age. The effectiveness of the proposed controller in various scenarios is validated based on numerical simulations. Extensive analyses prove that the FOPID-FLC outperforms the FOPID with a 90\% of improvement in terms of error performance indices and 20\% of improvement for the control action. Moreover, the controller exhibits improved robustness against uncertainties and disturbances encountered in rehabilitation environments.