Mode Control Research Articles

Development of an advanced current mode charging control strategy system for electric vehicle batteries

Electric vehicles (EVs) face customer hesitancy due to challenges in locating fast charging stations, lengthy recharging times, and incompatible charging ports. This research addresses these issues by proposing a novel current mode control strategy for EV battery charging. Traditional charging methods often result in suboptimal rates, battery degradation, and safety risks. The primary objective is to enhance charging efficiency, safety, and battery lifespan by optimizing parameters such as voltage and current. Control mode charging offers significant advantages over plug-in charging by minimizing stress factors that contribute to degradation, such as high temperatures and excessive charging cycles. This approach aims to extend the lifespan of EV batteries while ensuring safe, efficient, and fast charging. The control system offers three charging modes: slow (0.49 A, 6.31 W, 264 mins), medium (2.74 A, 34.85 W, 50 mins), and fast (4.62 A, 50.80 W, 30 mins) using a 12 V single-phase supply. This advanced strategy significantly improves EV charging system efficiency, with fast charging achieving 80% higher efficiency than slow charging in both simulations and experimental testing. The key contribution of this research is the development of a tailored current mode charging strategy that optimizes charging efficiency while ensuring battery longevity and safety.

IT2‐Fuzzy‐Model‐Based Guaranteed Cost Control for Continuous‐Time MJSs With Non‐Consecutive Transmission and Quantization

ABSTRACTThis article addresses the asynchronous guaranteed cost control (GCC) problems for interval type‐2 (IT2) fuzzy continuous‐time Markov jump systems (C‐TMJSs) with quantization and non‐consecutive transmission. A dynamic event‐triggered mechanism (ETM) for IT2 fuzzy C‐TMJSs is designed to address the limited bandwidth and communication burden, which incorporates mode‐dependent thresholds and weighting matrices. An asynchronous IT2 fuzzy guaranteed cost controller possessing unmatched premise variable information is developed to stabilize the considered system, achieving desired GCC performance. The relationship between system mode and controller mode is described by the hidden Markov model (HMM). A quantizer is employed to quantize the signal prior to transmission to the next node, modeled as a stochastic quantization utilizing the HMM. Furthermore, an improved mode‐dependent and fuzzy‐dependent Lyapunov function is constructed for stability analysis, and the conditions of optimal GCC performance are derived. Finally, two examples are performed to validate the effectiveness of the proposed algorithms.

An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder

Identification of light sensitivities, manifesting either as hyper-sensitive (over-stimulating) or hypo-sensitive (under-stimulating) in children with autism spectrum disorder (ASD), is crucial for the development of personalized sensory environments and therapeutic strategies. Traditional methods for identifying light sensitivities often depend on subjective assessments and manual video coding methods, which are time-consuming, and very keen observations are required to capture the diverse sensory responses of children with ASD. This can lead to challenges for clinical practitioners in addressing individual sensory needs effectively. The primary objective of this work is to develop an automated system using Internet of Things (IoT), computer vision, and data mining techniques for assessing visual sensitivities specifically associated with light (color and illumination). For this purpose, an Internet of Things (IoT)-based light sensitivities assessing system (IoT-LSAS) was designed and developed using a visual stimulating device, a bubble tube (BT). The IoT-LSAS integrates various electronic modules for (i) generating colored visual stimuli with different illumination levels and (ii) capturing images to identify children’s emotional responses during sensory stimulation sessions. The system is designed to operate in two different modes: a child control mode (CCM) and a system control mode (SCM). Each mode uses a distinct approach for assessing light sensitivities, where CCM uses a preference-based approach, and SCM uses an emotional response tracking approach. The system was tested on a sample of 20 children with ASD, and the results showed that the IoT-LSAS effectively identified light sensitivities, with a 95% agreement rate in the CCM and a 90% agreement rate in the SCM when compared to the practitioner’s assessment report. These findings suggest that the IoT-LSAS can be used as an alternative to traditional assessment methods for diagnosing light sensitivities in children with ASD, significantly reducing the practitioner’s time required for diagnosis.

Predicting human reliability for emergency fire pump operational process on tanker ships utilising fuzzy Bayesian Network CREAM modelling

An emergency fire pump is a critical equipment which assists the ship crew in handling extreme emergency situations involving fire on-board tanker ships. In case the main fire pump becomes ineffective during a fire, the emergency fire pump is used to extinguish the fire. In this case, it is necessary for the ship's crew to operate and use the pump without any failure. The aim of this study is to systematically predict human (crew) reliability for emergency fire pump operational processes on tanker ships since human dependability plays a significant role in safer shipment. To achieve this goal, fuzzy and BN (Bayesian Network) CREAM (Cognitive Reliability and Error Analysis Method) modelling is applied. CREAM is used to methodically estimate the probability of human error within a fuzzy set that accounts for uncertainties of CPC (Common Performance Condition), while BN is competent in estimating control modes in CREAM's reliability analysis. The paper's findings show ship crew reliability (9.08E-01) for the emergency fire pump operation process on tanker ships. These findings are expected to provide valuable information to prevent human errors and improve safety on tanker ships during firefighting, thereby reassuring the maritime industry of the potential for increased safety.

A 2DEG‐Based GaN‐on‐Si Terahertz Modulator with Multi‐Mode Switchable Control

AbstractAs terahertz (THz) technology has been widely considered as a key candidate for future sixth‐generation wireless communication networks, THz modulators show profound significance in wireless communication, data storage, and imaging. In special, dynamic tuning of THz waves through 2D electron gas (2DEG) incorporated with a hybrid design of metasurface has attracted keen interest due to the combined merits of deliberate structural design, rapid switching speed and the process compatibility. Current meta‐modulator enables very high modulation depth but encounter limited bandwidth. In this paper, by taking into account the co‐functional effects of temperature and voltage‐dependent dynamic control on transmission amplitude, a 2DEG‐based GaN‐on‐Si modulator with two switchable operational states (or four modes) of active wave control is proposed. Under cryogenic temperature conditions, the proposed device exhibits prominent 2D plasmons characteristics with switchable transitions between the gated mode and ungated mode for active control. Under room temperature conditions, the proposed device exhibits non‐resonance broadband spectra characteristics with tunable transitions between the linearity mode and depletion mode for transmission control. The scheme provides an option for the development of the actively tunable THz meta‐modulator and paves a way for the robust multifunctionality of electrically controllable THz switching, and biosensors.

Artificial intelligence control of flow separation from a curved ramp

This work aims to control flow separation from a two-dimensional curved ramp. The Reynolds number examined is Reθ = 5700 based on the momentum thickness of the turbulent boundary layer right before the ramp. Three steady jets, blowing tangentially along the ramp from three spanwise slits, are deployed at the most likely flow separation position, upstream and downstream of this position, respectively. Three different control modes are investigated, i.e., a single jet, multiple jets, and genetic algorithm-optimized blowing rates of three jets. The single jet placed at the time-averaged flow separation position is found to be most effective and efficient in eliminating flow separation among the first and second control modes. However, it is the third control mode that may not only eliminate the separation bubble completely but also cut down the energy consumption, by up to 30%, compared to the single jet blowing at the flow separation position. The flow physics underlying the control modes is also discussed.

The dual economics in the labour process: managerial contradictions and indirect control

One of the greatest impacts of Braverman’s Labour and Monopoly Capital is the discovery of a ‘control imperative’ within the capitalist production process. Whereas his equation of capitalist domination and Taylorism has been heavily criticized early on, the capabilities of the expanded use of digital technologies at the workplace have raised the question of whether a Taylorist mode of control is on the advance once again. The article challenges this perspective by addressing managerial problems that go beyond the problem to transform labour power into actual labour. Taking up Sohn-Rethel’s theory of ‘dual economics’, we argue that the necessity to reconcile contradictory requirements of the ‘economics of the market’ and the ‘economics of production’ poses an equally crucial challenge for management. Whereas that ‘problem of reconciliation’ remained latent in the Fordist era, tensions between the two logics of economics have now increasingly become a problem to solve within the course of controlling the labour process. Drawing on our own research on ‘the inner marketization of the firm’ over the last 15 years, we discuss ‘indirect control’ as a mode of control that precisely addresses the problem of reconciliation and considers recent changes in the course of digitalization. On the basis of our empirical findings, we describe the contradictory forms of activating and restricting subjectivity in the digital workplace and its implications for the legitimation of managerial power and capitalist domination.

Opto-chemogenetic inhibition of L-type CaV1 channels in neurons through a membrane-assisted molecular linkage.

Genetically encoded inhibitors of CaV1 channels that operate via C-terminus-mediated inhibition (CMI) have been actively pursued. Here, we advance the design of CMI peptides by proposing a membrane-anchoring tag that is sufficient to link the inhibitory modules to the target channel as well as chemical and optogenetic modes of system control. We designed and implemented the constitutive and inducible CMI modules with appropriate dynamic ranges for the short and long variants of CaV1.3, both naturally occurring in neurons. Upon optical (near-infrared-responsive nanoparticles) and/or chemical (rapamycin) induction of FRB/FKBP binding, the designed peptides translocated onto the membrane via FRB-Ras, where the physical linkage requirement for CMI could be satisfied. The peptides robustly produced acute, potent, and specific inhibitions on both recombinant and neuronal CaV1 activities, including Ca2+ influx-neuritogenesis coupling. Validated through opto-chemogenetic induction, this prototype demonstrates Ca2+ channel modulation via membrane-assisted molecular linkage, promising broad applicability to diverse membrane proteins.

Improved binary quantum-based Elk Herd optimizer for optimal location and sizing of hybrid system in micro grid with electric vehicle charging station

In recent times, there has been increasing interest in renewable power generation and electric vehicles within the domain of smart grids. The integration of electric vehicles with hybrid systems presents several critical challenges, including increased power loss, power quality issues, and voltage deviations. To tackle these challenges, researchers have proposed various techniques. Effective management of energy systems is essential for maximizing the benefits of integrating a hybrid system with a microgrid at an electric vehicle charging station. This research specifically aims to optimize the location and sizing of such a hybrid system within the microgrid. Additionally, an improved binary quantum-based Elk Herd optimizer approach is proposed. This approach addresses for optimally managing renewable energy sources and load uncertainty. The proposed system also considers the stochastic nature of electric vehicles and operational restrictions, encompassing diverse charging control modes. The proposed technique performance is implemented in MATLAB platform and compared against existing approaches. The analysis demonstrates the effectiveness in achieving optimal location and sizing for a hybrid system with an electric vehicle charging station. Additionally, the proposed approach contributes to minimizing power loss, electricity costs, and average waiting time. Furthermore, the proposed approach reduces computing time, net present cost, and emissions are 12.5 s, 1.1×106 dollar, 2.21×108 g year−1, respectively.

Research on the new control strategy of the three-phase VIENNA rectifier

Abstract The VIENNA rectifier has long been used in applications such as charging piles, and the theoretical research on its control strategy is enumerated. Compared with other circuit components, the VIENNA rectifier has fewer power components and a higher power factor, so it is applied to more application scenarios. The traditional PI control has a long start-up response time and a high overshoot, while the sliding mode control (SMC) has excellent control performance for nonlinear systems. In my article, the control mode of the VIENNA rectifier is analyzed mathematically, SMC takes controls of the current inner loop, and a new exponential reaching rate is put forward the at first improve the control process. Finally, a complete system simulation model is built to prove the superiority of the VIENNA rectifier based on an optimized SMC control strategy for each control target and the corresponding control strategy. The results show that the control effect is obviously optimized by using this method.

Hierarchical optimal operation for bipolar DC distribution networks with remote residential communities

To facilitate the seamless integration of renewable energy, bipolar DC distribution networks (Bi-DCDNs) have been widely adopted in various applications, including the Shenzhen Future Building, the Boeing 787 aircraft, and DC LED lighting systems in Singapore. Bi-DCDNs incorporate diverse flexible devices to improve both economic efficiency and security. However, a comprehensive coordination framework considering the regulatory heterogeneity among these flexible devices remains absent. Hence, this paper proposes a hierarchical coordination framework for flexible devices in Bi-DCDNs. More specifically, the upper-level model considers the operational differences of the DC transformer (DCT) in various modes to determine the optimal switching scheme for reducing losses; In the lower-level model, the control parameters of the DCT, energy storage systems (ESSs), and DC electrical springs (DCESs) are coordinated to enhance voltage quality. Furthermore, to accurately capture the steady-state behavior of flexible devices, the hierarchical framework incorporates the Newton-Raphson power flow method. This method formulates a steady-state model for multiple flexible devices and demonstrates the impact of different control modes of DCT on power flow. Subsequently, a genetic algorithm is used to solve the proposed model, ensuring that suboptimal decisions made at the upper level are rectified at the lower level, and vice versa. The numerical results indicate that the proposed framework achieves the optimal operation for both reduced losses and enhanced voltage quality in Bi-DCDNs. Furthermore, it exhibits advantageous applications for Bi-DCDNs with additional DCTs for remote residential communities.

A randomized control trial evaluating the advice of a physiological-model/digital twin-based decision support system on mechanical ventilation in patients with acute respiratory distress syndrome.

Acute respiratory distress syndrome (ARDS) is highly heterogeneous, both in its clinical presentation and in the patient's physiological responses to changes in mechanical ventilator settings, such as PEEP. This study investigates the clinical efficacy of a physiological model-based ventilatory decision support system (DSS) to personalize ventilator therapy in ARDS patients. This international, multicenter, randomized, open-label study enrolled patients with ARDS during the COVID-19 pandemic. Patients were randomized to either receive active advice from the DSS (intervention) or standard care without DSS advice (control). The primary outcome was to detect a reduction in average driving pressure between groups. Secondary outcomes included several clinically relevant measures of respiratory physiology, ventilator-free days, time from control mode to support mode, number of changes in ventilator settings per day, percentage of time in control and support mode ventilation, ventilation- and device-related adverse events, and the number of times the advice was followed. A total of 95 patients were randomized in this study. The DSS showed no significant effect on average driving pressure between groups. However, patients in the intervention arm had a statistically improved oxygenation index when in support mode ventilation (-1.41, 95% CI: -2.76, -0.08; p = 0.0370). Additionally, the ventilatory ratio significantly improved in the intervention arm for patients in control mode ventilation (-0.63, 95% CI: -1.08, -0.17, p = 0.0068). The application of the DSS led to a significantly increased number of ventilator changes for pressure settings and respiratory frequency. The use of a physiological model-based decision support system for providing advice on mechanical ventilation in patients with COVID-19 and non-COVID-19 ARDS showed no significant difference in driving pressure as a primary outcome measure. However, the application of approximately 60% of the DSS advice led to improvements in the patient's physiological state. clinicaltrials.gov, NCT04115709.

Delayed Recovery After Exercise-Induced Pain in People with Chronic Widespread Muscle Pain Related to Cortical Connectivity

Background/Objectives: There is a subset of patients with pain who become worse after exercise. To explore this, we examined the responses of people with chronic primary pain to a standardized high intensity exercise protocol used to induce delayed onset muscle soreness (DOMS). Methods: Ten participants with a diagnosis of chronic widespread muscle pain (CWMP) were matched by age and reported gender to ten participants without muscle pain (i.e., no pain (NP)). Participants completed a standardized DOMS protocol. Pain intensity in the arm at rest and with movement was assessed using daily electronic diaries. Peak pain, the timing of peak pain, and the time to recovery were compared between groups. Associations of pain variables with the functional connectivity of the sensorimotor (SMN), cerebellum, frontoparietal control (FPN), and default mode network (DMN) both within network nodes and the rest of the brain was assessed. Results: Significant differences in peak pain, the time to peak pain, and the time to recovery were noted between groups for both pain at rest and pain with movement after controlling for catastrophizing and pain resilience. Connectivity across the SMN, FPN, and DMN was associated with all pain-related variables. Significant group differences were identified between groups. Conclusions: A standardized muscle “injury” protocol resulted in more pain, a longer time to peak pain, and a longer time to resolve pain in the patient group compared to the NP group. These differences were associated with differences in connectivity across brain regions related to sensorimotor integration and appraisal. These findings provide preliminary evidence of the dysregulation of responses to muscle (micro)trauma in people with chronic pain.

Organizing the automated system of dispatch control over pump units at water pumping stations

The design and operation of modern dispatch control systems for pumping units involves comprehensive solution to separate engineering, technical, and scientific problems. The object of research is information processes enabling the operational modes of electrically driven pumping units at water pumping stations. This paper solves the scientific and technical task related to developing topology, hardware, and software tools, control algorithms, dispatch interface, and researching the modes of operation of pumping units in dispatch control systems at water pumping stations. Algorithms for controlling pumping units have been implemented, the advantage of which is the possibility of automated calculation of parameters for technological PID-controllers, taking into account the current electrical parameters of the frequency-controlled electric drive of pumping units and operating conditions. The WEB-oriented dispatch interface of the SCADA-based pumping unit control system was designed and tested, which enables control process in real time. Features of the developed dispatch control system are improved topology, expanded functionality, energy-saving control modes, and the possibility of further modernization based on the principles of standardization and unification of hardware and software tools and design procedures. The developed dispatch control system for pumping units at water pumping stations has been implemented and is successfully operated at an industrial water supply enterprise. The results provided for an increase in technical and economic indicators during the operation of technological equipment due to the efficiency of control and management procedures, energy-saving operational modes of the frequency-controlled electric drive of pumping units.

Quantum Effects Induced by Defects in Thin-Film Structures: A Hybrid Modeling Approach to Conductance and Transmission Analysis

This study investigated the possibility of quantum effects arising from defects resulting from the use of textronic electroconductive thin films and evaluated their impact on control characteristics. A hybrid model, where the classical approach to determine stationary fields based on the boundary element method was combined with a quantum mechanical approach using nonequilibrium Green’s functions, was created. The results of conductance and transmission coefficient simulations for different types of defects in the studied structure and a wide range of temperatures assuming two different control modes are presented. Based on the results, the conditions for the occurrence of quantum effects on the surface of conducting paths containing defects were specified, and their impact on conductance in the quantum mechanical approach was estimated.

Experimental Analysis of Flow Separation Control by a Dielectric Barrier Discharge Plasma Actuator in Burst-in-Burst Actuation Mode

This study investigated the effectiveness of a dielectric barrier discharge (DBD) plasma actuator operating in burst-in-burst (BIB) mode for flow separation control on a NACA 0015 airfoil. Time-resolved particle image velocimetry measurements were conducted at a Reynolds number of 66,000 and 13° angle of attack. Various BIB signal configurations were tested, with actuation periods of 70 ms and 150 ms, non-actuation periods ranging from 5 ms to 50 ms, and burst frequencies of 300 Hz and 600 Hz. Proper orthogonal decomposition was applied to analyze the flow field dynamics. The results showed that BIB actuation maintained flow attachment with reduced power consumption compared with continuous burst actuation. However, the effectiveness was highly sensitive to the BIB parameters, with some configurations failing to achieve consistent reattachment and becoming unstable. This study reveals complex interactions between actuation vortices and separation processes, highlighting both the potential and challenges of intermittent plasma actuation for efficient flow control.

Intelligent Control Framework for Improving Energy System Stability Through Deep Learning-Based Modal Optimization Scheme

Ensuring the stability of power systems is essential to promote energy sustainability. The integrated operation of these systems is critical in sustaining modern societies and economies, responding to the increasing demand for electricity and curbing environmental consequences. This study focuses on the optimization of energy system stability through the coordination of power system stabilizers (PSSs) and power oscillation dampers (PODs) in a single-machine infinite bus energy grid configuration that has flexible AC alternating current transmission system (FACTS) devices. Intelligent control strategies using PSS and POD techniques are suggested to increase power system stability and generate supplementary control signals for both the generator excitation system and FACTS device switching control. An intelligent optimal modal control framework equipped with deep learning methods is introduced to control the generator excitation system and thyristor-controlled series capacitor (TCSC). By optimally choosing the weighting matrix Q and implementing close-loop pole shifting, an optimal modal control approach is formulated. To harness its adaptive potential in fine-tuning controller parameters, an auxiliary deep learning-based optimization algorithm with actor–critic architecture is implemented. This comprehensive technique provides a promising path to effectively reduce electromechanical oscillations, thereby enhancing voltage regulation and transient stability in power systems.

An Exploratory Study of Contextual Control Modes in Teamwork.

This study investigated the relationship between human team member contextual control and team performance under time constraints. Contextual control modes, which describe different strategies for action selection in dynamic environments, characterize how humans maintain performance under variable demands. Control modes have not yet been studied in teamwork settings. Modeling of the cross-level interaction between team members' control modes and emerging team behaviors can improve understanding of effective teamwork in dynamic environments. A human-subjects study explored the relationship between individual contextual control and team performance. Questionnaires about contextual control were used to elicit individual control modes. Analysis compared team members' control modes and investigated how control modes changed under varying time pressures. Participant's control modes differed in their look ahead horizon, the extensiveness of prior action evaluation, and their prior experience. Many team members shifted control modes during trials, resulting in both convergence and divergence of paired control modes. No effects on communication rate were found due to changes in team members' control modes, but partially significant findings may suggest that the control mode divergence affects performance. Teams can operate in multiple control mode configurations that change dynamically according to context. Further research with an increased sample size is warranted to analyze how time constraints influence team members' control modes and overall teaming processes and whether divergence of team control mode is favorable under time pressures. Further study of contextual control in teams may help improve team design to better support teams in coping with time constraints in dynamic environments.

Early-stage Testing of Thermal Power Dispatch Simulator for Subjective Mental Workload and Evolution Time

The excess thermal energy produced by nuclear power plants (NPPs) during low electricity demands can be utilized in industrial processes, such as hydrogen production, through a thermal power dispatch (TPD) system. Initial testing of the first iteration of a single-train TPD design with a manual control mode at the Idaho National Lab (INL) revealed a high operator workload and degraded control capability. The current study evaluated the impact of an enhanced dual-train TPD design on operators’ subjective mental workload and evolution task-time while completing two operating scenarios in manual and automatic control modes. The results showed no statistically significant difference between participants’ mental workload using both control modes. Evolution time in automatic control mode took a shorter time than in manual control, with participants completing all evolutions in less than the 10-min set as the design specification limit. The shorter evolution time is discussed within the context of plant safety and operational efficiency.

FusionOC: Research on optimal control method for infrared and visible light image fusion

Infrared and visible light image fusion can solve the limitations of single-type visual sensors and can boost the target detection performance. However, since the traditional fusion strategy lacks the controllability and feedback mechanism, the fusion model cannot precisely perceive the relationship between the requirements of the fusion task, the fused image quality, and the source image features. To this end, this paper establishes a fusion model based on the optimal controlled object and control mode called FusionOC. This method establishes two types of mathematical models of the controlled objects by verifying the factors and conflicts affecting the quality of the fused image. It combines the image fusion model with the quality evaluation function to determine the two control factors separately. At the same time, two proportional-integral-derivative (PID) control and regulation modes based on the backpropagation (BP) neural network are designed according to the control factor characteristics. The fusion system can adaptively select the regulation mode to regulate the control factor according to the user requirements or the task to make the fusion system perceive the connection between the fusion task and the result. Besides, the fusion model employs the feedback mechanism of the control system to perceive the feature difference between the fusion result and the source image, realize the guidance of the source image feature to the entire fusion process, and improve the fusion algorithm's generalization ability and intelligence level when handling different fusion tasks. Experimental results on multiple public datasets demonstrate the advantages of FusionOC over advanced methods. Meanwhile, the benefits of our fusion results in object detection tasks have been demonstrated.