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The purpose of this manuscript was to design a better method for recovery from rhegmatogenous retinal detachment (RRD) surgery. We attempted to achieve this by designing a helmet that can manipulate intraocular magnetic nanoparticles (MNPs) and create a magnetic tamponade, eliminating the need for postoperative head positioning. A simulated analysis was developed to predict the pattern of magnetic force applied to the magnetic nanoparticles by external magnetic field. No participants were involved in this study. Instead, magnetic flux and force data for three different helmet designs were collected using virtual simulation tools. A prototype helmet was then constructed and magnetic flux and force data were recorded and compared to virtual data. For both virtual and physical scenarios, magnitude and direction of the resulting forces were compared to determine which design created the controlled direction and strongest forces into the back of the eye. Of the three virtual designs, both designs containing a visor had greater force magnitude than magnet alone. Between both designs with visors, the visor with bends resulted in forces more directed at the back of the eye. The physical prototype helmet shared similar measurements to virtual simulation with minimal percent error (Average = 5.47%, Standard deviation = 0.03). Of the three designs, the visor with bends generated stronger forces directed at the back of the eye, which is most appropriate for creating a tamponade on the retina. We believe that this design has shown promising capability for manipulating intraocular MNPs for the purpose of creating a tamponade for RRD.

This paper introduces a systematic and comprehensive method to design a constrained fuzzy model predictive control (MPC) cooperated with integral sliding mode control (ISMC) based on the Takagi-Sugeno (T-S) fuzzy model for uncertain continuous-time nonlinear systems subject to external disturbances. The proposed controller benefits from the robustness, optimality, and practical constraints considerations. The robustness against the uncertainties and matched external disturbances is achieved by the proposed ISMC without iterative calculation for obtaining the robust invariant set. The MPC schemes are designed separately based on the both quadratic and non-quadratic Lyapunov functions. By the proposed MPC, the states of the system reach the desired values in the optimal, constrained, and robust manner against the unmatched external disturbances. New linear matrix inequalities (LMIs) conditions are proposed to design both the proposed MPC schemes. Also, the practical constraints on the control signals are guaranteed in the design procedure based on the invariant ellipsoid set. To evaluate the effectiveness of the suggested strategy, some simulation and experimental tests were run.

This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the total energy cost. This framework tracks both generation and load variations to decide optimal power generations and the exchange of power with the utility. A linear cost function is defined for the utility where the rates are updated at every time interval. In addition, a realistic approach is taken to limit the power generation from renewable energy sources, including photovoltaics (PVs), wind turbines (WTs), and dispatchable distributed generators (DDGs). The maximum output power of DDGs is limited to their ramp rates. Besides this, a specific cloud-fog architecture is suggested to make the real-time operation and monitoring of the proposed method feasible for utility-connected and islanded microgrids. The cloud-fog-based framework is flexible in applying demand response (DR) programs for more efficiency of the power operation. The algorithm’s performance is examined on the 14 bus IEEE network and is compared with optimal results. Three operating scenarios are considered to model the load as light and heavy, and after denial of service (DoS) attack to indicate the algorithm’s feasibility, robustness, and proficiency. In addition, the uncertainty of the system is analyzed using the unscented transformation (UT) method. The simulation results demonstrate a robust, rapid converging rate and the capability to track the load variations due to the probable responsive loads (considering DR programs) or natural alters of load demand.

This paper presents an analysis on ways to improve the annual energy production (AEP) of a wind turbine utilizing a drivetrain that operates based on the hydrostatic transmission. The system configuration of such a drivetrain is explained in details and a comparison of operation and characteristics with existing drivetrains is provided. AEP was estimated for these configurations through appropriate dynamic modeling and operational efficiency optimization. Optimal selection of a number of design variables and system parameters contributed to the improvements in the AEP. Findings of this study demonstrate that the proposed hydrostatic drivetrain improves the AEP of a 750 kW turbine by up to +8% when compared with a geared wind turbine. The AEP improvements of the hydrostatic drive wind turbine were more than 10% for a 1.5 MW system over geared configuration. It is also demonstrated that the efficiency of power generation can be improved under various wind speeds. The suitable selection of synchronous speed of the generator directly improves the efficiency of operation by up to 35% at low wind speeds. An efficiency improvement was also observed under higher operating pressures and longer turbine blades.

Integration of Compressed Air Energy Storage (CAES) system with a wind turbine is critical in optimally harvesting wind energy given the fluctuating nature of power demands. Here we consider the design of a CAES for a wind turbine with hydrostatic powertrain. The design parameters of the CAES are determined based on simulation of the integrated system model for a combination of these parameter values, namely the compression ratios of the air compressors and the expanders and the air tank size. The results of the simulations were used to choose the best design parameters, which would produce the best stable performance through increased energy output of the integrated CAES and wind turbine based on the intermittent wind profile. Simulation results for a 600 kW rated power wind turbine with integrated CAES indicate that increasing the tank size and compression ratio will improve the overall power quality through increased energy output up to a limit beyond which the power quality exhibits only marginal improvement.

Electrochemical model-based condition monitoring of a Li-Ion battery using an experimentally identified battery model and Hybrid Pulse Power Characterization (HPPC) cycle is presented in this paper. LiCoO2 cathode chemistry was chosen in this work due to its higher energy storage capabilities. Battery electrochemical model parameters are subject to change under severe or abusive operating conditions resulting in, for example, Navy over-discharged battery, 24 h over-discharged battery, and overcharged battery. Stated battery fault conditions can cause significant variations in a number of electrochemical battery model parameters from nominal values, and can be considered as separate models. Output error injection based partial differential algebraic equation (PDAE) observers have been used to generate the residual voltage signals in order to identify these abusive conditions. These residuals are then used in a Multiple Model Adaptive Estimation (MMAE) algorithm to detect the ongoing fault conditions of the battery. HPPC cycle simulated load profile based analysis shows that the proposed algorithm can detect and identify the stated fault conditions accurately using measured input current and terminal output voltage. The proposed model-based fault diagnosis can potentially improve the condition monitoring performance of a battery management system.