Search Results (1,475)

The integration of photovoltaic (PV) generation with bidirectional electric vehicle (EV) fast-charging systems offers a promising pathway toward sustainable transportation and grid support. However, the dynamic coupling between maximum power point tracking (MPPT) perturbations and grid-side power quality presents a fundamental challenge in such multi-converter architectures. This paper addresses this challenge through a coordinated design and optimization framework for a grid-connected, PV-assisted bidirectional off-board EV fast charger. The system integrates a 184.695 kW PV array via a DC-DC boost converter, a common DC link, a three-phase bidirectional active front-end rectifier with an LCL filter, and a four-phase interleaved bidirectional DC-DC converter for the EV battery interface. A comparative evaluation of three MPPT algorithms establishes the Fuzzy Logic Variable Step-Size Perturb & Observe (Fuzzy VSS-P&O) as the optimal strategy, achieving 99.7% tracking efficiency with 46$\mathrm{s}$ settling time. However, initial integration of this high-performance MPPT reveals system-level harmonic distortion, with grid current total harmonic distortion (THD) reaching 4.02% during charging. To resolve this coupling, an Artificial Bee Colony (ABC) metaheuristic algorithm performs coordinated optimization of all critical PI controller gains. The optimized system reduces grid current THD to 1.40% during charging, improves DC-link transient response by 43%, and enhances Phase-Locked Loop (PLL) synchronization accuracy. Comprehensive validation confirms robust bidirectional operation with seamless mode transitions and compliant power quality. The results demonstrate that system-wide intelligent optimization is essential for reconciling advanced energy harvesting with stringent grid requirements in next-generation EV fast-charging infrastructure. Full article

High wind power penetration aggravates power system inertia scarcity, and wind turbines switching to MPPT mode after virtual inertia support induces secondary frequency drop (SFD), impairing grid frequency stability. Traditional energy storage system (ESS) sizing methods fail to couple wind turbine virtual inertia dynamics, rotor kinetic energy recovery and time-varying wind speeds, causing a trade-off between regulation performance and economy. To address this, an optimal ESS sizing method for wind-storage coordinated frequency regulation is proposed, including a doubly fed induction generator (DFIG) model for virtual inertia-power drop correlation, an incomplete compensation strategy, and a constrained three-objective optimization model co-optimizing virtual inertia and ESS parameters. The method, solved by NSGA-II with fuzzy membership functions, is validated on a 1000 MVA grid with a 245 MW DFIG wind farm. Results show it mitigates SFD, avoids ESS over-sizing, and balances performance and economy, breaking the decoupling between traditional ESS sizing and the virtual inertia dynamics of wind turbines. Full article

It is inconceivable that an unsafe mode of transport could be sustainable. Therefore, in this century, where sustainability is at the forefront, occupational health and safety is more important than ever. However, the most used traditional risk analysis methods cannot be applied to every area due to their shortcomings. In this study, a new proposal of interval-valued intuitionistic fuzzy failure mode and effects analysis (IVIF FMEA) within the framework of fuzzy logic overcomes the inadequacies of the traditional method, particularly in uncertainty assessment. In this proposed IVIF FMEA method, the effects of historical incident data are incorporated to improve risk prioritization through static decision-maker weighting and dynamic risk parameter weighting. Then, this novel method is implemented for the operational risks of the underground stations within a metropolitan city in Türkiye. When the proposed method is compared with traditional FMEA and classical IVIF FMEA, failure modes (FMs) related to platform surveillance, working at height, and chemical handling are identified as requiring priority action. To ensure safer and more sustainable transport operations, the prioritization of safety measures is as important as their implementation. The obtained results further offer a transferable and methodologically robust basis for advancing sustainability-oriented safety governance by enabling more evidence-based risk prioritization within urban transportation systems. Full article

Unmanned spherical robots are autonomous mobile platforms with a fully enclosed spherical shell, providing high stability and strong adaptability to complex terrains. However, existing pendulum or flywheel spherical robots often suffer from limited maneuverability, whereas complex hybrid actuation schemes tend to compromise system stability. To address these issues, this study proposes an improved pendulum-driven spherical robot with a mechanically decoupled actuation design, integrating a pendulum system and a circular gear rack turning mechanism. This design enables smooth linear rolling as well as rapid in-place rotation, significantly enhancing maneuverability and motion flexibility on complex terrains. A dynamic model of the spherical robot is established to describe the decoupled actuation mechanism, and a fuzzy proportional–derivative (PD) control strategy is designed for rolling and steering control. Simulation and prototype experiments were conducted to evaluate trajectory tracking, steering response, and terrain adaptability. The results demonstrate that the proposed spherical robot achieves path following and in-place turning with robust mobility. Full article

This study focuses on the zonotope-based set-membership estimation issue for switched Takagi–Sugeno (T-S) fuzzy systems with application to tunnel diode circuits. Given the practical importance of tunnel diodes in radio-frequency, microwave, and high-speed electronic systems, we first model the tunnel diode circuit as a switched T-S fuzzy system to characterize its inherent dynamics. To address the state estimation issue, we propose a zonotopic set-membership estimation framework for the system under mode-dependent average dwell-time (MDADT) switching, which enables tighter state bounding while ensuring $H_{\infty }$ robustness. A mode-dependent observer is designed to attenuate the effects of external disturbances and measurement noise, and the stability of the estimation error system is analyzed based on an appropriate Lyapunov function. Numerical simulations are conducted and the corresponding results show that the estimated boundary can accurately encompass the true state of the system, and the volume of the estimated set is reduced by approximately 28.99% compared with the interval observer method, thus demonstrating the effectiveness and potential of the proposed approach. Full article

Five-axis Computerized Numerical Control (CNC) machine tools play a pivotal role in the precision manufacturing of aeroengine turbine blades, where ultra-high reliability and accuracy are essential. Failure Mode, Effects and Criticality Analysis (FMECA) has been widely applied in the reliability assessment of such advanced machining systems due to its systematic evaluation of potential failure modes. However, traditional FMECA approaches often overlook the ambiguity of human cognition and the interdependence among expert evaluations, limiting their effectiveness in complex aerospace manufacturing environments. To address these issues, this paper proposes a novel FMECA framework based on generalized intuitionistic linguistic theory. A new Generalized Intuitionistic Linguistic Weighted Geometric Average (GILWGA) operator is introduced to couple multi-source expert information and quantify the fuzziness inherent in subjective assessments. Additionally, an intuitionistic linguistic entropy-based weighting scheme is developed to dynamically evaluate key risk factors, including severity, occurrence, detectability, and controllability. The proposed framework is applied to a case study involving the spindle system of a five-axis CNC machine tool used in aeroengine blade production. The results demonstrate that the proposed method offers more robust and consistent failure mode prioritization, providing effective decision support for reliability-centered maintenance in aerospace equipment manufacturing. Full article

This paper presents an efficient stand-alone photovoltaic water pumping system (PVWPS) intended for agricultural irrigation applications, operating without energy storage. The system employs a three-phase induction motor supplied by a three-level neutral point clamped (NPC) inverter. The proposed control strategy integrates the advantages of two distinct controllers to enhance both energy extraction and drive performance. On the photovoltaic side, a fuzzy logic-based maximum power point tracking (MPPT) algorithm is implemented to ensure continuous operation at the global maximum power point under rapidly varying irradiance conditions. On the motor drive side, a direct torque control (DTC) scheme is combined with the multilevel NPC inverter to regulate electromagnetic torque and stator flux. The use of a multilevel inverter significantly mitigates the inherent drawbacks of conventional DTC, notably torque and flux ripples, as well as stator current harmonic distortion. The overall control architecture maximizes power transfer from the photovoltaic generator to the pumping system, resulting in improved dynamic response and energy efficiency. The proposed system is validated through detailed MATLAB/Simulink simulations under abrupt irradiance variations and a realistic daily solar profile corresponding to August conditions in Kairouan, Tunisia. Simulation results demonstrate substantial performance improvements, including an 88% reduction in torque ripples, a 50% decrease in flux ripple, a 77.9% reduction in stator current THD, and a 33.3% enhancement in speed transient response compared to conventional DTC-based systems. Full article

Fault Detection and Diagnosis (FDD) in complex industrial systems requires methods that can handle uncertain operating conditions, soft thresholds, evolving sensor behavior, and increasing volumes of heterogeneous data. Traditional model-based or rule-driven approaches offer interpretability but lack adaptability, while purely data-driven and Large Language Model (LLM)-based methods often struggle with consistency, traceability, and causal grounding. Dynamic Master Logic (DML) provides a causal and temporal reasoning structure with fuzzy rules that capture gradual drift, soft limits, and asynchronous sensor signals while preserving traceability and deterministic evidence propagation. Building on this foundation, this paper presents a DML–LLM hybrid architecture that integrates targeted LLM inference to interpret unstructured information such as logs, notes, or retrieved documents under controlled prompts that maintain domain constraints. The combined system integrates Bayesian updating, deterministic routing, and semantic interpretation into a unified FDD pipeline. In a semiconductor manufacturing case study, the proposed framework reduced time to detection (TTD) from 7.4 h to 1.2 h and improved the F1 score from 0.59 to 0.83 when compared with conventional Statistical Process Control (SPC) and Fault Detection and Classification (FDC) workflows. Provenance completeness increased from 18% to 96%, while engineer triage time was reduced from 72 min to 18 min per event. These results demonstrate that the hybrid framework provides a scalable and explainable approach to anomaly detection and fault diagnosis in sensor-rich industrial environments. Full article

In order to tackle resilient supplier selection (RSS) of high uncertainty in resilient supply chain management, an effective correlation coefficients-based multicriteria group decision-making (MCGDM) methodology has been constructed. The major contribution of the present study is twofold. Firstly, in view of that extant criteria systems are all in lack of theoretical rationality, this paper establishes a capabilities-based analytical framework for intensive evaluation of supplier resilience by taking processual viewpoints of dynamic capabilities theory and risk management theory. Secondly, to empower the proposed correlation coefficients-based MCGDM methodology, probabilistic dual hesitant fuzzy uncertain unbalanced linguistic set (PDHF_UUBLS) is employed to capture hybrid uncertainties in decision processes of RSS. Then, theoretically compliant correlation coefficients (CCs) for PDHF_UUBLS are developed, including statistics-based CC, information energy-based CC and their weighted versions. Especially, information energy-based CCs overcome limitations of statistics-based CCs in special cases, thus exhibiting general applicability. In addition, a compatibility-based programming model has also been developed to objectively derive an unknown weighting vector for DMUs. Furthermore, illustrative case studies and comparative experiments have been carried out to verify effectiveness and stability of the proposed methodology. Taken together, this paper satisfies the new normal demand of resilience building in supply chain management and presents an effective MCGDM methodology for handling the key problems of RSS. Full article

Permanent magnet synchronous motors (PMSMs) are utilized in demanding conditions and applications requiring precision and accuracy, such as servo systems. Especially at low speeds, the effects of cogging torque, current measurement and offset errors, improper controller gains, mechanical resonance, and torque fluctuations caused by load torque and flux result in fluctuations at various frequencies in the motor output speed. This study, motivated by two factors, proposes an extended state observer (ESO)-based multivariable fast response extremum-seeking (FESC) interval type-2 fuzzy PID (IT2FPID) controller to improve dynamic response and reduce speed ripple at low speeds in situations where all these negative factors could arise. This approach enables the real-time adaptation of parameters to counteract the decline in controller performance caused by the nonlinear characteristics of PMSMs and parameter fluctuations while also optimizing disturbance rejection in the speed response under varying operating conditions and existing speed ripple. The experimental results from the prototype setup validate that the proposed control mechanism is functional, valid, and precise in diminishing speed ripples during low-speed operations. The simulation and test outcomes of the control scheme show that speed noise at low speeds is reduced from 26% to 3% compared to traditional proportional-integral (PI) controller and supertwisting (STW) sliding mode controller (SMC) responses and that the scheme exhibits a 16–23% reduction in undershoot amplitude and faster recovery in the presence of load torque variations. Full article

Load forecasting for Integrated Energy Systems (IESs) is critical to enabling multi-energy coordinated optimization and low-carbon scheduling. Facing multi-load types and multi-site high-dimensional heterogeneous data, there remains a global learning challenge stemming from insufficient representation of spatiotemporal coupling features. In response to the multi-source heterogeneous characteristics of IES loads, this paper designs a Spatiotemporal Topology Encoder that maps load data into a tensorized multi-energy spatiotemporal topological representation via fuzzy classification and multi-scale ranking. In parallel, we construct a MultiScale Hybrid Convolver to extract multi-scale, multi-level global spatiotemporal features of multi-energy load representations. We further develop a Temporal Segmentation Transformer and a Steady-State Exponentially Gated Memory Unit, and design a jointly optimized forecasting model that enforces global dynamic correlations and local, steady-state preservation. Altogether, we propose a multi-scale spatiotemporal fusion and steady-state memory-driven load forecasting method for integrated energy systems (MSTF-SMDN). Extensive experiments on a public real-world dataset from Arizona State University demonstrate the superiority of the proposed approach: compared to the strongest baseline, MSTF-SMDN reduces cooling load RMSE by 16.09%, heating load RMSE by 12.97%, and electric load RMSE by 6.14%, while achieving R² values of 0.99435, 0.98701, and 0.96722, respectively, confirming its feasibility, efficiency, and promising potential for multi-energy load forecasting in IES. Full article

This paper investigates dynamic event-triggered control (DETC) and encoding–decoding schemes to achieve the synchronization of T-S fuzzy neural networks (FNNs). DETC allows the transmission signals to be controlled aperiodically during the actual operation of the system, enabling a rapid response to practical control tasks. Meanwhile, during the event-triggered control process, an encoding–decoding scheme with externally injected noise is used to protect the signals. First, a dynamic event-triggered control mechanism is established, and an encoding–decoding scheme is used to optimize the transmission of controller signals. Subsequently, the Lyapunov–Krasovskii functional is constructed to derive the system’s synchronization criteria and calculate the controller gains. Finally, numerical simulation experiments are conducted to verify the effectiveness and feasibility of the proposed method. Full article

This work presents the real-world deployment of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for intelligent energy control in resource-constrained IoT devices. The proposed system employs a first-order Takagi–Sugeno fuzzy model with three Gaussian membership functions per input: ambient temperature, light intensity, and battery voltage. The model was trained offline using augmented environmental datasets and subsequently translated into optimized embedded C code for execution on an ESP32 microcontroller. The controller dynamically adjusts the node’s deep sleep duration according to environmental conditions, enabling adaptive behavior based solely on local environmental conditions without requiring external connectivity. A 10-day field deployment compared the ANFIS controller with conventional fixed and rule-based strategies. Results show that the ANFIS-based strategy reduced energy consumption by 31.1% relative to the fixed approach while maintaining accurate adaptation to environmental conditions (RMSE = 9.6 s). The inference process required less than 2.5 ms and used under 30 KB of RAM, confirming the feasibility of real-time fuzzy inference on resource-constrained embedded platforms. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

This paper examines the dynamics of positive solutions to a system of fuzzy difference equations, which provide effective tools for modeling dynamical systems with uncertain or imprecise parameters. The main objective is to establish the existence, uniqueness, and qualitative properties of positive solutions within a fuzzy framework. After recalling some fundamental notions from fuzzy set theory, we analyze the dynamics of the proposed system. The main results prove the existence of a unique positive fuzzy solution under suitable conditions and establish the boundedness, continuity, and convergence of the solutions. In particular, all solutions converge to a unique positive equilibrium point. Numerical experiments for $(l_1,l_2)=(2,3)$ and $(l_1,l_2)=(4,1)$ with uncertainty levels $\gamma =0.2$ and $\gamma =0.8$ illustrate the theoretical results and confirm the convergence toward the unique positive equilibrium. Full article