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Planar magnetic components are compact and less susceptible to skin effect due to their thin copper layers. However, the increase in parasitic capacitance has been a challenge in high-frequency operation of planar transformers (PTs). Parasitic capacitances resonate with inductance and may damage switch devices. Thicker printed circuit board (PCB) and reconfigured windings are suggested in this paper to mitigate the parasitic capacitances. A detailed analysis of the parasitic capacitances is performed with a prototype PT. The resonant frequency increased from 1.27 to 1.63 MHz with a 0.4 mm thicker PCB. The reconfigured PT was 1.38 MHz, 0.3 MHz higher than the original PT.

Over the past decades, various control methodologies have been studied to improve the performance of motion control systems. However, such methods have no general guidelines for controller gain tuning or performance optimization in the presence of nonlinearities and/or uncertainties. Linear parameter-varying (LPV) control methods have recently been studied to improve the performance of motion control systems by treating the nonlinearities as parameter variations. By contrast, with motion control systems, the plant, microprocessors, controllers, actuators, and sensors are interconnected using a communication network, where the network inherently induces communication delays while sending and receiving information. Hence, when designing an LPV control system, it is necessary to consider both communication delays and nonlinearities. In this study, we address an LPV-approach based H8control problem for motion control systems under communication delays between the actuator and plant, and between the sensor and plant. We obtained a linear matrix inequality (LMI) condition under which a closed-loop system using the proposed controller satisfies the H8control performance and stability when subjected to a communication delay. The main technique applied to obtain the corresponding LMI condition for an LPV system is interpolating the scheduling variables of the nonlinear parameter variations at each vertex. Finally, we applied the proposed control method to the driver set of a permanent magnet synchronous motor and verified the H8control performance under a communication delay.

In this paper, we propose a robust arbitrary-time path tracking control using reduced order kinematic model for the unmanned ground vehicle. The proposed method consists of the robust arbitrary-time convergence control and fixed-time super-twisting observer for the kinematic model, and the fixed-time super-twisting controller of the dynamic model. To apply the robust arbitrary-time convergence control to the unmanned ground vehicle, the reduced order kinematic model is developed to move the sensor location to the origin through coordinate transformation. The robust arbitrary-time convergence control is designed to obtain the arbitrary-time convergence and the robustness for the kinematic model. When the tracking errors exceed the predefined error boundary after the predefined arbitrary-time, the tracking errors can converge to zero within predefined arbitrary-time by the switching rule. The fixed-time super-twisting observer is designed to estimate the lumped disturbance for the kinematics model. The fixed-time super-twisting controller is developed for the dynamic model.

A two-transformer active clamp forward flyback (ACFF) converter without output inductors is suitable for high output current applications such as low-voltage DC-DC converters (LDCs) in electrified vehicles. This paper presents the reverse power transmission operation of an ACFF converter used Synchronous Rectifier (SR). One of the target applications of the proposed converter is bidirectional LDCs used in electrified vehicles due to its high-power density and cost competitiveness. Operational principles with key voltage and current waveforms are provided, and a detailed analysis of each operating interval is provided, offering guidelines for component selection. Experimental results obtained from a 14 V–600 V hardware prototype verifies the feasibility of operation in practical LDCs.

This paper proposes a new averaged switch model for pulse-width modulated (PWM) single-ended primary-inductor converter (SEPIC) operating in discontinuous conduction mode (DCM). The equivalent series resistance (ESR) of each energy component is considered to achieve a more precise model. The model, which is modification of the conventional averaging equation, accurately reflects both the remaining current and the actual charge on each capacitor in DCM. The proposed model utilizes a new duty ratio constraint and predicts better than previous models at high frequencies. The dependent sources of the switch components are derived from the modified averaged equation, and an implementation of the model in LTspice XVII is provided to facilitate the frequency response analysis. The frequency response of a prototype SEPIC in DCM, with an input voltage of 5 V, an output voltage of 13.5 V, and a switching frequency of 100 kHz, was experimentally measured to verify the proposed model. Simulation and experimental results demonstrate that the proposed averaged switch model solves the discrepancies at high frequencies, which was a limitation of previous models.

Several iPSC-derived three-dimensional (3D) cultures have been generated to model Alzheimer’s disease (AD). While some AD-related phenotypes have been identified across these cultures, none of them could recapitulate multiple AD-related hallmarks in one model. To date, the transcriptomic features of these 3D models have not been compared with those of human AD brains. However, these data are crucial to understanding the pertinency of these models for studying AD-related pathomechanisms over time. We developed a 3D bioengineered model of iPSC-derived neural tissue that combines a porous scaffold composed of silk fibroin protein with an intercalated collagen hydrogel to support the growth of neurons and glial cells into complex and functional networks for an extended time, a fundamental requisite for aging studies. Cultures were generated from iPSC lines obtained from two subjects carrying the familial AD (FAD) APP London mutation, two well-studied control lines, and an isogenic control. Cultures were analyzed at 2 and 4.5 months. At both time points, an elevated Aβ42/40 ratio was detected in conditioned media from FAD cultures. However, extracellular Aβ42 deposition and enhanced neuronal excitability were observed in FAD culture only at 4.5 months, suggesting that extracellular Aβ deposition may trigger enhanced network activity. Remarkably, neuronal hyperexcitability has been described in AD patients early in the disease. Transcriptomic analysis revealed the deregulation of multiple gene sets in FAD samples. Such alterations were strikingly similar to those observed in human AD brains. These data provide evidence that our patient-derived FAD model develops time-dependent AD-related phenotypes and establishes a temporal relation among them. Furthermore, FAD iPSC-derived cultures recapitulate transcriptomic features of AD patients. Thus, our bioengineered neural tissue represents a unique tool to model AD in vitro over time.

In this article, we propose the fuzzy event-triggered super twisting sliding mode control (STSMC) approach for improving the position tracking performance and reducing the communication resource under various unknown disturbances in the control system of permanent magnet synchronous motors (PMSMs). The event-triggered strategy is designed to minimize the unnecessary use of computation/communication resources while achieving acceptable control performances. However, when the unexpected disturbances are injected, the tracking performance could be degraded since the conventional event-triggered strategy using previous tracking error information cannot immediately update the control input according to rapid unexpected disturbances. To resolve this problem, we design the fuzzy rule, which is applied in the event-triggered strategy to adjust the updating time based on the tracking error at the present time to achieve robustness against unexpected disturbances. Thus, a fuzzy event-triggered control input can be transmitted immediately to the PMSM when an unexpected disturbance occurs. The STSMC method is designed to enhance the position tracking performance under disturbances. The stability of the closed loop is shown using the modified Lyapunov theory. In addition, the nonexistence of Zeno execution is guaranteed by analyzing the minimum bounded event-triggered interval time. The effectiveness of the proposed method was verified through simulations and experiments, where the experiment was conducted using a PMSM testbed.

In this article, a robust nonlinear torque control method using the steering wheel torque (SWT) model is proposed for electric power steering (EPS) systems. We develop an EPS model using SWT as the state variable to directly control the SWT. A robust nonlinear torque control method is designed to improve the SWT control performance of EPS. The proposed method consists of an extended state observer and a nonlinear sliding mode control (SMC) method. The extended state observer is designed to estimate the lumped disturbance, which includes modeling errors and external disturbances for robustness. The SMC is designed to track the desired SWT and suppress the estimation error. The nonlinear gain is proposed for reducing the chattering in the SMC. The proposed method is designed based on the SWT model; thus, it can improve the torque control performance while remaining robust to parameter uncertainty and external disturbances. The performance of the proposed method is validated via experiments using a pinion type EPS hardware-in-the-loop simulation.

In this study, a prior-selected time convergence (PTC) optimization algorithm is proposed to obtain the optimal solution to RTEDPs in prior-selected time (PT) with mathematical convergence analysis. The prior-selected convergence time is independent of initial conditions and any problem parameter. To the best of the author's knowledge, this is the first time an PTC optimization algorithm is introduced, which obtains the optimal solution for the dual optimization problem of RTEDPs in PT. The implementation of the proposed solution is simple, and only one tuning parameter is required using the adaptive learning rate. The numerical simulations were conducted using an IEEE 118 bus grid under various situations such as constraint variations, load variations, and generator plug-off. The simulation results demonstrate that the proposed method always guarantee PTC in 10 iterative simulations with random initial conditions and different coefficients of local objective function while satisfying the demand-supply energy balance.

In practice, steering wheel torque (SWT) control performance for an electric power steering (EPS) system may degrade owing to various reasons, such as model/parameter uncertainties and unpredictable large external disturbances (e.g., reaction force). Furthermore, the desired SWT suddenly varies and its sign also changes because the direction of the steering wheel angle (SWA) often changes according to the driver. In this article, a neural network (NN) approximation-based adaptive nonlinear control (ANC) using reinforced gain (RG) for an EPS system is proposed to resolve the above-mentioned problems using an SWT model. The proposed control method employs an NN approximator, ANC, and RG. The NN approximator is designed to estimate the unknown complex nonlinear function in EPS modeling. The ANC is designed via backstepping to compensate for parametric uncertainties and external disturbances. Further, the RG is designed as a positive nonlinear function of the error to sufficiently suppress the error according to variations in the desired SWT and external disturbance. The RG increases to suppress the error at a rapid transient response, owing to the sudden change in the sign of the desired SWT and the external disturbance. Thus, a high gain (increased RG) is used only when necessary so that an unnecessarily high gain can be avoided.

In this study, an adaptive neural network (NN) control using nonlinear information (NI) gain for permanent magnet synchronous motors (PMSMs) is proposed to improve control and estimation performance. The proposed method consists of a nonlinear controller, a three-layer NN approximator, and NI gain. The nonlinear controller is designed via a backstepping procedure for position tracking. The commutation scheme is designed to implement the PMSM control without the direct-quadrature (DQ) transform. The three-layer NN approximator is designed to estimate the unknown complex function generated by the recursive backstepping process. The NI gains are designed to enhance the control and estimation performance according to the increased tracking errors owing to the load torque and the desired position variations. All of signals in the closed-loop system guarantee the semiglobal uniformly ultimately boundness (UUB) using the Lyapunov stability theorem and the input-to-state stability (ISS) property. The performance of the proposed method was validated by experiments performed using a PMSM testbed.

The performance of backstepping control with an external disturbance guarantees the upper bound of the tracking error, which is determined by an unknown upper bound of the disturbance and design parameters. Therefore, tuning the design parameters is necessary to obtain a satisfactory control performance. Generally, a high gain is used to reduce the tracking error under the unknown disturbance in the previous methods. However, the use of an unnecessarily high gain may amplify the high-frequency measurement noise and peaking phenomenon. In this study, we propose an adaptive learning gain (ALG)-based nonlinear control for a strict-feedback nonlinear system with an external disturbance to achieve the desired control performance without any information about the disturbance and gain tuning. An ALG-based nonlinear controller using a backstepping procedure is designed to track the desired trajectory. The ALG update law is established to suppress the tracking error within the desired bound. The amplification of the measurement noise in the control input is reduced, as the ALG is updated to avoid the use of an unnecessarily high gain when the controller achieves more than the desired performance.

Objective:At-risk youth benefit from supportive, high-quality relationships with fathers and father figures, but relatively little is known about the factors that influence the quality of these relationships. Accordingly, this study aims to identify predictors and examine moderating effects of father type, father involvement, and exposure to intimate partner violence (IPV) on the quality of father–child relationships. Method:We conducted generalized estimating equations using a sample (N= 422) of 12-year-olds from the Longitudinal Studies of Child Abuse and Neglect data set. Results:Adolescents living with a biological father reported significantly higher quality father–child relationships than those living with a nonbiological father. Greater father involvement was associated with higher quality father–child relationships. The positive association between father involvement and father–child relationship quality was significantly stronger for adolescents living with a nonbiological father. Additionally, exposure to IPV was negatively related to father–child relationship quality for nonbiological fathers, but not for biological fathers. Conclusions:Findings indicate the need to offer more specialized interventions and programs to support coresiding nonbiological fathers’ unique challenges in building high-quality relationships with nonbiological children.

Background: The cervical collar has been used as a common device for the initial stabilization of the cervical spine. Although many cervical collars are commercially available, there is no consensus on which offers the greatest protection, with studies showing considerable variations in their ability to restrict cervical range of motion. The use of the XCollar (Emegear, Carpinteria, CA) has been known to decrease the risk of spinal cord injury by minimizing potential cervical spinal distraction. We compared XCollar with two other cervical collars commonly used for adult patients with cervical spine injury to evaluate the difference in effectiveness between the three cervical collars to restrict cervical range of motion.Objectives: This study aimed to evaluate the difference between the three cervical collars in their ability to restrict cervical range of motion.Method: A total of 30 healthy university students aged 21–25 years participated in this study. Participants with any cervical disease and symptoms were excluded. Three cervical collars were tested: Philadelphia®Collar, Stifneck®Select™Collar, and XCollar. A digital camera and an image-analysis technique were used to evaluate cervical range of motion during flexion, extension, bilateral bending and bilateral axial rotation. Cervical range of motion was evaluated in both the unbraced and braced condition.Results: XCollar permitted less than a mean of 10° of movement during flexion, extension, bilateral bending and bilateral axial rotation. This was less than the movement permitted by the other two cervical collars.Conclusion: XCollar presented superior cervical immobilization compared to the other two commonly used cervical collars in this study. Thus, when cervical collar is considered for an adult patient with cervical spine injury, XCollar might be one of the considerate options as a cervical immobilization device.

This paper proposes an extended state observer-based robust position tracking control for DC motor with external disturbance and system uncertainties. In this control strategy, external disturbance and acceleration dynamics, including uncertainty of the input parameter, are lumped in the disturbance using acceleration dynamics. An extended state observer (ESO) is designed to estimate the disturbance and full states, whereas a position tracking controller is designed to compensate for the disturbance. The stability of a closed-loop system is investigated using Lyapunov theory. The proposed method guarantees the semi-global uniform ultimate boundedness of the position tracking error using only position feedback for unknown nonlinear systems with external disturbance. The proposed method is simple and robust against external disturbance and parameter uncertainties. In addition, only nominal parameter values of the input gain are required. The proposed method was experimentally verified, and it exhibited improved position tracking performance compared to other methods using state feedback and ESO-based conventional DC motor models.

In this paper, we consider the super-twisting observer-based sliding mode control algorithm with fuzzy variable gains (STOSMC) for the fully-actuated hexarotor. Our hexarotor has full actuation due to six titled propellers that allows to control position and orientation (attitude) simultaneously, and resolves the singularity problem of the rotational matrix by using the quaternion modeling framework. We show that the proposed STOSMC for the hexarotor guarantees finite-time convergence of the estimation error and asymptotic stability of the hexarotor. In simulations, we demonstrate the nonsingularity and fully-actuated control performance of the hexarotor by considering extreme position and attitude control scenarios. Moreover, the simulation results show that the hexarotor achieves the fast and precise tracking performance to the desired position and the desired attitude and the chattering phenomenon is reduced compared with the fixed-gains observer-based super-twisting sliding mode control due to the fuzzy mechanism. [ABSTRACT FROM AUTHOR]

This study derived a novel reduced second-order model for an autonomous lane keeping system. The proposed reduced model of the lateral vehicle motion has the following two advantages: first, one can control the vehicle’s lateral motion with only simple linear second-order dynamics and second, the state variable of the reduced model includes look-ahead distance likewise human driver. The backstepping control for the lateral control and the compensation of the system parameter and uncertainties is developed using the reduced model. Moreover, the reduced model-based sliding mode observer is designed to estimate the lateral velocity. The stability of the closed-loop system is proven using passivity. The lateral control performance of the proposed method is validated via numerical simulations using CarSim and MATLAB/Simulink and compared to the fourth-order lateral motion model-based linear quadratic controller.

We propose a torque overlay based robust steering wheel angle control of electric power steering (EPS) for lateral control using backstepping design. The main contribution of this paper is that the proposed method is designed based on torque overlay and that the global uniform ultimate boundedness of the steering wheel angle tracking error is guaranteed using only steering wheel angle feedback with external disturbances. The key idea is to make the EPS dynamics be simplified. Then, the external disturbances, system function, and input gain uncertainty are regarded as a disturbance. An augmented observer is designed to estimate the full state and the disturbance. A nonlinear damping controller is developed via backstepping to suppress a position tracking error using input-to-state stability property. The proposed method uses only steering wheel angle feedback and nominal value of the input gain. The proposed method is simple to implement in real-time control and robust to the parameter uncertainties and the external disturbances. Since the proposed method is designed based on torque overlay as add-on type, it can be integrated with the conventional EPS system facilitating driver's intervention.

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GGA3-mediated BACE1 accumulation in dystrophic neurites is an early pathogenic event and is independent of Aβ extracellular deposition.

Due to an unfortunate oversight the acknowledgement has been omitted