Search Results (1,434)

Fast charging of lithium-ion battery packs involves a compromise between charging speed, temperature rise, and aggressive current profiles that may accelerate battery degradation. This paper presents a current-stress-aware fuzzy logic control framework for safe fast charging of series-connected lithium-ion battery cells. The proposed controller uses a physically interpretable two-input, one-output fuzzy structure in which the highest cell-voltage difference, $V_d$, and the lowest single-cell voltage, $V_B$, are used to determine the charging-current command, $I_{\mathrm{charge}}$. Unlike conventional fuzzy charging approaches that rely on manually selected membership functions or weighted single-objective tuning, the proposed method simultaneously optimizes the Gaussian membership-function parameters and the input/output scaling gains using a Pareto-based multi-objective optimization framework. The resulting design vector contains 21 decision variables, including 18 membership-function parameters and three scaling gains. Three conflicting objectives are minimized: the time required to reach 95% state of charge, the maximum temperature rise above the reference temperature, and a normalized current-stress index based on the integral of the squared charging current. The framework is implemented in MATLAB/Simulink using a three-cell Panasonic NCR18650PF lithium-ion battery pack model. The obtained Pareto front reveals the expected trade-off between fast charging and battery protection. The fastest solution reaches 95% SOC in 5440 s but produces the highest temperature rise and current-stress index, whereas the selected knee-point controller reaches the target in 6880 s while reducing the maximum temperature rise and current-stress index compared with the fastest solution. Robustness tests under variations in initial SOC, cell imbalance, initial temperature, capacity scaling, and internal-resistance scaling show that the knee-point controller maintains stable charging behavior and satisfies the imposed thermal safety constraint. The results demonstrate that the proposed current-stress-aware Pareto-optimized fuzzy controller provides a systematic and interpretable approach for balancing charging speed, thermal safety, and battery stress in lithium-ion battery fast charging. Full article

Digital transformation is reshaping the value creation and competitive logic of human resource service (HRS) firms, yet its antecedents are commonly examined as independent net effects. This study adopts an exploratory mixed-method design to identify and test configurational pathways to digital transformation. First, grounded theory analysis of interviews with 20 HRS firms in Beijing identified five antecedent conditions: digital technology application, digital strategic planning, firm social capital, the digital policy environment, and competitive intensity. Second, fuzzy-set qualitative comparative analysis (fsQCA) was applied to survey data from 97 firms. The analysis yielded five solution terms for high digital transformation and two configurations for non-high digital transformation. The five high-outcome solution terms were organized into three broader configurational patterns: technology–strategy synergy, technology-driven competition transformation, and strategy–ecosystem synergy. The non-high-outcome configurations reflected either weak digital foundations combined with ecosystem disengagement or relational-resource maintenance accompanied by transformation inertia. No single condition was necessary for either outcome, and the configurations associated with high and non-high digital transformation exhibited causal asymmetry. The findings show that digital transformation in HRS firms depends on the alignment of technological, organizational, and environmental conditions rather than on technology investment alone. Full article

The increasing use of autonomous mobile robots (AMRs) in internal logistics systems improves operational efficiency. However, it also introduces challenges related to safety, reliability, sensor-based monitoring and human–robot interaction. This study proposes a sensor-driven Digital Twin and fuzzy decision-support framework for operational risk monitoring in AMR-based transportation systems. The proposed approach integrates Digital Twin technology with fuzzy logic methods to support continuous sensing, operational data acquisition, and data-driven risk evaluation in autonomous logistics environments. In the proposed framework, the Digital Twin acts as a continuous monitoring and early-warning environment. It enables continuous observation of system states, robot condition, navigation performance, traffic intensity and operational disturbances. To support decision-making under uncertainty, the fuzzy Analytic Hierarchy Process (fuzzy AHP) is applied to determine the relative importance of selected safety and reliability indicators. These indicators include condition monitoring parameters, mean time between failures, sensor-related disturbances and task completion performance. Subsequently, a hierarchical Mamdani fuzzy inference system is used to evaluate the operational risk level of the transportation system based on aggregated KPI values derived from Digital Twin data. The applicability of the proposed approach is illustrated through a case study involving multiple AMRs operating in a dynamic intralogistics environment. The results indicate that the integration of sensor-based Digital Twin monitoring with fuzzy decision-support mechanisms improves operational risk visibility and supports more effective risk identification and management in Industry 4.0 intralogistics systems. Full article

Fuzzy set theory, proposed by Lotfi Zadeh, is applied with great success in embedded, control-oriented systems based on microcontrollers or programmable logic devices. Algorithms used in these systems operate on fuzzy sets represented by membership functions. In practically implemented control systems, these functions typically possess simple linear shapes, such as trapezoidal or triangular. To compute the function value, the classical method relies on multiplication and division. These operations are complex to implement in software or hardware, consume significant resources, and are time-consuming. This paper proposes a fixed-point computation method to obtain the value of a membership function without relying on these operations. For this purpose, the bisection algorithm was adopted, replacing its complex arithmetic with addition and binary shift operations. This approach allows for a reduction in computation time and the hardware or software overhead required by resource-constrained embedded systems, especially architectures without dedicated hardware accelerators for multiplication and division. Sample software and hardware implementations demonstrate its suitability for real-world systems. Full article

This paper investigates practical finite-time adaptive fuzzy control for uncertain nonlinear systems subject to full-state constraints and unmeasured state variables. To deal with the inaccessibility of some state variables, an observer is constructed. A state-dependent nonlinear mapping is introduced to ensure that all system states remain within their prescribed bounds. In contrast to typical barrier Lyapunov function (BLF)-based schemes, the presented method manages full-state constraints without imposing any extra prerequisites on virtual control signals. Fuzzy logic systems (FLSs) act as estimators for the unknown nonlinearities emerging in the control law design, while the integration of dynamic surface control techniques helps circumvent the “complexity explosion” characteristic of conventional backstepping approaches. Subsequently, a practical finite-time adaptive fuzzy tracking controller is constructed, which guarantees the semi-global practical finite-time stability of the closed-loop system, with all closed-loop signals remaining bounded for all time and the tracking error converging to a residual set within finite time. Simulation results demonstrate that the tracking error enters a small residual set within finite time and that all system states remain within their prescribed constraints. Full article

Induction motors remain the workhorse of modern industry thanks to their robustness, cost effectiveness and high efficiency, but the growing demands of electrified transport, high-performance automation and Industry 4.0 impose increasingly stringent control requirements. This paper presents an integrated, application-oriented survey of control strategies for high-performance induction motor drives, covering classic scalar V/f control as a baseline and advanced field-oriented control (FOC), direct torque control (DTC), model predictive control (MPC), nonlinear/robust schemes and intelligent/data-driven and digital twin-assisted solutions. The methods are analyzed within a unified framework in terms of dynamic response, torque and flux ripple, current harmonic distortion, efficiency, robustness, implementation complexity and suitability for sensorless and fault-tolerant operation. Emphasis is placed on hybrid strategies that combine classical vector or DTC structures with MPC, fuzzy and neuro-fuzzy logic, neural network-based observers, reinforcement learning and digital twin-enabled monitoring to reconcile fast dynamics with high efficiency, low ripple and lifecycle reliability. Consolidated comparison tables and a hybrid control map highlight typical performance trends, trade-offs between simplicity and performance, and the complementary roles of AI and digital twins as system-level enablers. The survey also outlines promising research directions toward systematically designed hybrid controllers, lightweight digital twins for embedded platforms and experimentally validated benchmarks that can accelerate the industrial uptake of next-generation induction motor drives. Full article

Cultural heritage sites are affected by environmental and anthropogenic pressures that require decision-analysis methods capable of combining heterogeneous datasets while accounting for uncertainty. Multi-Criteria Decision Analysis (MCDA), particularly when integrated with Geographic Information Systems (GIS) and Earth Observation (EO) data, is widely used in geospatial analysis involving multiple, often conflicting criteria. This review examines the evolution, application domains, and methodological challenges of MCDA in cultural heritage risk assessment. The literature indicates a predominant reliance on weighting-based methods, especially the Analytic Hierarchy Process (AHP) combined with GIS-based weighted overlay techniques, while uncertainty treatment, temporal monitoring, validation, and multi-threat applications remain limited. Three illustrative applications show that asset-level, regional susceptibility, and historic-urban frameworks address complementary decision needs but differ in their data, expertise, and institutional requirements. Recent developments show a trend to combine MCDA with fuzzy logic, machine learning, and uncertainty modeling, although methodological consistency across these approaches remains uneven. The findings suggest that multi-criteria risk assessment for cultural heritage may depend less on introducing new analytical techniques and more on improving the integration of existing methods. Incorporating repeatable environmental observations, sensitivity analyses, multi-threat assessment, and stakeholder participation may support a more coherent and reproducible approach to heritage-risk assessment. Full article

Energy storage systems (ESSs) play a crucial role in mitigating the intermittency and variability of renewable energy sources (RESs) and enhancing the stability and reliability of modern power systems. However, the inherent limitations of individual storage technologies, particularly the trade-off between energy density and power density, restrict their ability to satisfy diverse operational requirements. In this context, hybrid energy storage systems (HESSs), which combine complementary storage technologies, such as batteries, supercapacitors, and flywheels, have emerged as an effective solution capable of simultaneously delivering high-energy and high-power performance. This paper presents a comprehensive review of HESS architectures, converter topologies, energy management strategies (EMSs), and applications. The EMS taxonomy is organized into classical and intelligent control. Classical EMS approaches are categorized into filtration-based, rule-based, deadbeat, droop, sliding mode, and fuzzy logic control, whereas intelligent EMS approaches encompass optimization-based methods, including model predictive control, as well as learning-based techniques such as supervised and reinforcement learning. Moreover, HESS applications are examined across grid-scale systems, microgrids, renewable energy systems, transportation, power quality improvement, frequency regulation, peak shaving, and uninterruptible power supply systems. Representative implementations are also reviewed to identify current technological trends, operational challenges, and performance trade-offs. Finally, future research directions are outlined, with emphasis on digital twins, privacy-preserving and explainable learning frameworks, cyber–physical security, and adaptive and scalable EMSs. Full article

The rapid growth of cloud and distributed computing systems has increased the complexity of real-time request distribution under dynamic and multi-factor conditions. In such environments, load-balancing decisions must simultaneously consider uncertain and interdependent parameters, including server load, response time, and resource capacity. Fuzzy logic is an effective tool for modeling such uncertainty; however, the expansion of linguistic variables often leads to a rule-explosion problem, which increases computational complexity and reduces the real-time applicability of fuzzy load-balancing systems. This study proposes an explainable multi-objective quantum-inspired fuzzy optimization approach for scalable load balancing in complex computing environments. The proposed model integrates fuzzy inference with a Grover-inspired classical search strategy to optimize the selection of fuzzy rule subsets. The Grover-inspired component is implemented as a classical simulation rather than a gate-based quantum circuit. A multi-objective evaluation function is formulated to jointly assess rule accuracy, coverage, interpretability, and compactness. This formulation enables the model to reduce redundant fuzzy rules while preserving decision transparency and maintaining reliable load distribution performance. The proposed approach is evaluated in a simulated cloud computing environment with heterogeneous servers and dynamic request arrival patterns. Comparative experiments are conducted against classical load-balancing strategies, conventional fuzzy load balancing, and evolutionary fuzzy optimization methods, including GA-FLB and PSO-FLB. The experimental results show that the proposed model reduces the size of the fuzzy rule base while maintaining competitive response time, load distribution quality, SLA compliance, and decision interpretability. These findings indicate that the integration of Grover-inspired classical search mechanisms with fuzzy reasoning provides a promising direction for developing scalable, compact, and explainable load-balancing models for next-generation intelligent computing systems. Full article

To address the challenges in risk prevention and control of post-earthquake gas explosions in high-rise buildings and the deficiencies of traditional methods in handling uncertainty, this paper conducts a risk evolution analysis from the perspectives of fuzzy Bayesian networks (FBNs) and complex network (CN) theory. First, based on comprehensive risk factor identification, an earthquake-gas explosion disaster chain evolution model was constructed. Subsequently, the nodes and logical relationships of the disaster chain were mapped through Bayesian network (BN) topology, with fuzzy set theory employed to determine prior and conditional probability parameters for causal reasoning and risk diagnosis. Finally, complex network (CN) centrality metrics were introduced to quantify node topological importance, and chain-cutting disaster mitigation strategies were proposed accordingly. The research results indicate that gas overrun (M2) is the node with the highest comprehensive importance, while sensitivity analysis further confirms that it remains the most critical controllable node for interrupting the disaster chain. This method effectively reveals the disaster evolution mechanism and provides a scientific reference for disaster prevention and mitigation decision-making in high-rise buildings. Full article

Atomic force microscopy (AFM) has emerged as a powerful platform for quantifying nanoscale mechanical properties of cells, tissues, and extracellular matrix (ECM) components, providing candidate biomarkers for disease diagnosis, classification, prognosis, and treatment monitoring. However, the clinical translation of AFM-based nanomechanical biomarkers remains limited by low throughput, operator dependence, complex force-curve interpretation, heterogeneous biological samples, and the lack of standardized analytical pipelines. Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used to address these limitations by enabling automated AFM image and force-curve analysis, multiparametric feature extraction, cell and tissue classification, quality control, and high-throughput mechanophenotyping. This review summarizes how AI and ML have already been applied to AFM-derived nanomechanical and morphological data, with emphasis on cancer, fibrotic disease, and treatment response monitoring. We discuss classical ML models, deep learning approaches, clustering, fuzzy logic methods, and emerging automated Bio-AFM workflows. We further highlight current limitations, including small datasets, limited external validation, lack of reproducible reporting standards, and insufficient integration with clinical metadata. Finally, we propose a roadmap for AI-enabled AFM mechanobiomarkers, focusing on standardized datasets, explainable models, multimodal mechano-optical imaging, and clinically relevant validation strategies. Full article

This paper investigates the predefined-time leader–follower consensus tracking problem for multiple Euler–Lagrange systems subject to actuator faults under a fractional-order dynamic event-triggered framework. First, to guarantee the convergence-time requirement, nonsingular predefined-time terms are embedded into the backstepping design, such that the consensus tracking errors can enter a small residual set within a predefined time. Then, a fractional-order dynamic event-triggered mechanism is introduced to determine the update instants of the sampled actuator input, in which the fractional-order auxiliary variable is used to incorporate historical triggering information into the threshold dynamics. Moreover, adaptive laws and fuzzy logic systems (FLSs) are employed to compensate for actuator faults and model uncertainties. Furthermore, based on predefined-time Lyapunov analysis, it is proved that all closed-loop signals are bounded and predefined-time consensus tracking performance is achieved. Finally, simulation results verify the effectiveness of the proposed method. Full article

This study presents a systematic taxonomic review of risk modelling and assessment methods in construction projects over the past 35 years (1990–2025). Through a structured four-stage process, 91 peer-reviewed articles from 15 leading journals were analysed. The taxonomic approach enabled the classification and mapping of methods according to chronological evolution, study type, authorship patterns, and focus areas, while thematic analysis was employed to synthesise key themes, trends, and research gaps. The review examines publication trends, geographical distribution of research contributions, and methodological developments. The findings reveal that the probability-impact (P-I) model remains the dominant approach, despite its well-documented limitations in capturing risk interdependencies and their cascading effects on project quality and overall performance. Fuzzy Set Theory (FST), Analytic Hierarchy Process (AHP), and Monte Carlo Simulation (MCS) emerged as the most frequently adopted techniques. The analysis demonstrates a clear evolution in the field: from predominantly basic probabilistic methods in the 1990s to increasingly sophisticated hybrid, fuzzy logic-based, and AI-enhanced approaches after 2010. Notwithstanding these advancements, significant gaps persist, particularly the lack of integrated frameworks capable of simultaneously addressing risks across multiple project objectives—cost, time, quality, and performance. This review synthesises the state of knowledge in the field, identifies persistent theoretical and practical shortcomings, and offers a comprehensive roadmap for future research. Key directions include the development of machine learning applications, dynamic modelling techniques, and holistic multi-objective risk assessment frameworks to better align risk management theory with the complex realities of modern construction projects. Full article

In recent years, the potential of exoskeletons to enhance human capabilities has attracted significant research interest. Nevertheless, the control of Rehabilitation Lower-Limb Exoskeletons (RLLEs) is challenging because of their strong nonlinear behaviour. In the paper, a Grey Fuzzy Sliding-Mode (GFSM) controller, which is designed based on the optimization accuracy and estimation capability of the fuzzy logic system, was used for trajectory tracking of a RLLE’s joints. This paper presents the tuning of the controller parameters optimally using Grey Wolf Optimization (GWO) integrated with an Interval Type-2 Fuzzy Logic System (IT2FLS) in real-time. The GFSM was selected as the controller law, in which initially, GWO was used to tune the parameters based on the estimated RLLE’s mathematical model. The optimal tuned parameters were employed to determine the defuzzification range of the fuzzy logic system. IT2FLS was provided to tune the real-time controller parameters. The performance of the GFSM was validated by human-RLLE experiments which showed superior performance compared to other conventional controllers. The experimental results show that the controller achieved reductions in the average error of 81.8%, 82.9%, 84.1%, and 80.6%, respectively, compared with conventional adaptive control methods. These findings indicate that the GFSM can be used to improve motor function recovery in individuals with hemiplegia. By integrating biomechanically inspired motion assistance with IT2FLS, our proposed GFSM controller contributes to the development of biomimetic rehabilitation exoskeletons capable of reproducing natural human gait. Full article

The integration of renewable energy into smart grids is beneficial for a sustainable future and the environment. Still, it has challenges such as energy optimisation, environmental conditions and power quality degradation. Existing Maximum Power Point Tracking (MPPT) techniques often focus on tracking efficiency under steady-state conditions, ignoring the impact of real-time variation in environmental conditions and load. The predictive power flow control (PPFC) algorithm is available with one or more fixed MPPT algorithms. No studies have reported on how the choice of MPPT affects PPFC harmonic mitigation. This paper addresses both concerns through a systematic comparative analysis of MPPT techniques integrated with a PPFC method to mitigate harmonics in renewable-integrated smart grid systems. To address this research gap, a comprehensive comparative analysis of various MPPT techniques, such as Perturb and Observe (P&O), Incremental Conductance (INC), Fuzzy Logic Control (FLC), and hybrid Machine Learning (ML) techniques, integrated with PPFC to achieve effective harmonic mitigation in a smart grid environment is conducted. A 3 MW solar farm integrated with a battery storage system is modelled in MTALB/Simulink 2025b under real-time varying conditions, such as environmental and load variations over time in Auckland, New Zealand. The study focuses on key performance parameters such as total harmonic distortion (THD), power loss, stability and efficiency. The Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller, integrated with forecast-based power flow control, achieved overall performance by providing higher efficiency (97.5%), effective harmonic mitigation, and enhanced system stability under the nonlinear behaviour of the photovoltaic system. The proposed ANFIS-based system ensured a stable and smooth power output under varying environmental conditions, outperforming conventional and other intelligent MPPT techniques. Full article