Search Results (1,462)

Offshore wind farms are increasingly and rapidly expanding due to their ability to harness strong and consistent wind energy resources. Large offshore wind farms are connected to mainland grids through High-Voltage Direct Current (HVDC) technology. However, offshore wind farms can often experience disturbances related to sudden wind changes, voltage drops/dips, faults related to converter switching, and unbalanced grid conditions which affect both the HVDC operation and wind turbine output. As a result, there is a growing need for more advanced and reliable modeling and monitoring tools. Moreover, traditional proportional-integral (PI) controllers are widely applied in wind turbines and HVDC systems due to their simple structure, easy implementation, and reliability. However, PI controllers perform poorly under non-linear and abnormal/fast-changing conditions, especially during sudden drops in wind power and grid faults. With this background, this paper first develops a digital twin model of an offshore wind farm that enables remote operation and monitoring of individual wind turbines. Also, an artificial intelligence (AI)-based controller, namely a fuzzy logic controller (FLC), is proposed to maintain transient stability of a full digital twin-based offshore wind farm connected to the HVDC grid under fault conditions. The effectiveness of the proposed FLC is demonstrated by considering a digital twin-enabled 700 MW offshore wind farm. The performance of the proposed FLC has been compared with that of the PI controller. Simulations performed by the MATLAB/Simulink software show that during the moderate voltage dip at 15 s, the PI controller experienced a 29.8% power reduction with a recovery time of approximately 9 s, whereas the FLC reduced the power drop to 23.1% and recovered within 6 s. During the severe converter disturbance at 15 s, the PI controller recorded a 36.9% power reduction compared to 23.4% for the FLC. Similarly, during the short-duration turbulence at 15 s, the PI controller exhibited a 36.73% power drop and recovered in approximately 7 s, while the FLC limited the power reduction to 19.17% and recovered within 5s. Overall, the FLC provided improved voltage stability, faster recovery, reduced oscillations, and superior fault ride-through capability compared with the conventional PI controller, demonstrating its effectiveness for digital twin-enabled offshore wind farm application. Full article

In light of the rapid development of the electric vehicle market, permanent magnet synchronous motors (PMSMs) are becoming essential components of propulsion systems. This is due to their high efficiency, remarkable power density, and ability to deliver high torque over a wide speed range. However, the optimal control of these motors under dynamic conditions remains a major challenge due to system nonlinearities, parameter variations, and external disturbances. Conventional strategies such as field-oriented control (FOC), direct torque control (DTC), and fuzzy logic control (FLC) show variable performance in terms of current quality, robustness, and energy efficiency. To overcome these limitations, this study proposes an intelligent control strategy based on artificial neural networks (ANNs), which ensures efficient operation and high control performance under various operating conditions. This approach leverages the learning capabilities of deep neural networks to improve control accuracy, system stability, and overall energy performance. The results obtained show a significant reduction in the current’s total harmonic distortion (THD) as well as an improvement in the stator’s current quality and the electromagnetic torque’s dynamic behavior compared to conventional methods. This improvement reduces overall losses in the electric drive system, thereby contributing to increased vehicle energy efficiency. As a result, the electric vehicle’s range is extended, and the dynamic performance of the PMSM is optimized. These results confirm the potential of artificial intelligence techniques for developing intelligent, robust, and adaptive control systems designed for modern electric propulsion applications. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

Private Networks or Standalone Non-Public Networks (SNPNs) are essential for Industry 4.0 and enterprise connectivity. However, most existing studies rely on simulations, evaluate only a single radio access technology, or report raw key performance indicators (KPIs) without an interpretable quality assessment framework. In practical deployment, operators require measurement-driven evidence to assess the performance and feasibility of 4G LTE and 5G SNPN solutions. This study presents a controlled experimental comparison of software-defined radio (SDR)-based 4G LTE and 5G SNPNs using the same Universal Software Radio Peripheral (USRP) platform, Open5GS, srsRAN, and commercial off-the-shelf user equipment (COTS-UE). The evaluation was conducted in an indoor environment under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. Experimental iPerf3 results show that the SDR-based 5G SNPN achieves higher downlink and uplink throughput than the SDR-based 4G LTE SNPN across all tested scenarios. The 5G deployment reaches up to 55 Mbps downlink and 40.5 Mbps uplink under LOS conditions, while maintaining 42 Mbps downlink and 28 Mbps uplink under NLOS conditions. Furthermore, 5G achieves lower latency than 4G LTE, with average values ranging from 21 ms to 31 ms. To provide interpretable network quality assessment, a Mamdani fuzzy logic-based Network Quality Index (NQI) with 81 inference rules is proposed to map signal-to-interference-plus-noise ratio (SINR), throughput, latency, and jitter into linguistic quality levels. The proposed approach enables nonlinear integration of heterogeneous KPIs and provides a technology-agnostic framework for practical SNPN deployment. Full article

In modern power systems such as new energy generation and smart grids, inverters serve as core equipment for electrical energy conversion and transmission. Their operational reliability directly impacts system power supply quality and safety stability. Currently, research on inverter fault diagnosis technology primarily focuses on linear load conditions, with diagnostic method design and validation based on linear load characteristics. However, with the rapid advancement of power electronics technology, power electronic loads such as variable frequency drives, charging stations, and distributed power sources are increasingly prevalent in power systems. These loads exhibit nonlinear and time-varying characteristics under complex operating conditions, leading to a growing variety of inverter faults with significantly diversified and complex fault signatures. Traditional diagnostic methods fail to adapt to the unique characteristics of power electronic loads, making it difficult to accurately identify various faults. Consequently, they no longer meet the diagnostic demands of practical engineering scenarios. In addition, current diagnostic methods for open-circuit power transistors, intermittent faults, and sensor faults often employ different approaches, which consume significant controller resources and are prone to mutual interference, leading to false triggers. This paper takes a three-phase four-wire inverter as the research subject. Targeting the challenge of fault diagnosis under power electronic load conditions, it proposes a comprehensive diagnostic method capable of simultaneously diagnosing power switch open circuits, intermittent faults, and current sensor faults. First, the characteristics of various faults are analyzed. Subsequently, fault diagnosis variables are constructed using the actual arm voltage of the inverter and the ideal arm voltage. Logical rules for each type of fault are established, and diagnosis is performed through fuzzy logic inference. Finally, experiments validated the effectiveness of this fault diagnosis scheme, with open-circuit faults detected in less than 2 ms, intermittent faults in less than 0.5 ms, and sensor faults in less than 3 ms. Full article

The objective of this research was to develop an intelligent stress mapping and a smart control platform, utilizing Artificial Intelligence (AI), to increase the fatigue life of a hydraulic crane. The crane’s boom was modeled and co-simulated using ANSYS, ADAMS, and MATLAB. A flexible model of the boom was created in ANSYS and then exported to ADAMS. Stress analysis was performed using the maximum principal hotspot method and the von Mises yield criterion. Stress optimization was conducted using a Neural Network (NN) algorithm, which is a key implementation of AI in this study. Two control platforms, one based on Neural Networks and another on Fuzzy Logic, were designed to apply AI in controlling the crane’s movements. The Neural Network algorithm optimized the crane’s movement by adjusting velocity at critical positions where structural stress was high, while the fuzzy logic-based control algorithm utilized stress feedback from the crane’s structure. Both AI-driven control algorithms were integrated into the physical crane in the lab, and extensive testing demonstrated a significant increase in the crane’s fatigue life, along with effective damping of crane vibrations. This paper introduces a novel AI-driven approach combining Neural Networks and Fuzzy Logic for intelligent stress mapping and control, specifically tailored for hydraulic cranes. Unlike previous works, this research integrates real-time stress feedback into the control process and validates the algorithms through experimental implementation on a prototype crane, significantly improving its fatigue life. Full article

This study presents a hybrid controller that integrates fuzzy logic control and the Incremental Conductance method. This controller optimizes maximum power point tracking in a 330 W photovoltaic system by designing a DC-DC converter. The study evaluates how the number and distribution of membership functions, specifically three-, five-, and seven-function configurations, affect system performance using the Integral Square Error (ISE) and Integral Absolute Error (IAE) indices. The empirical results demonstrate that the seven-function architecture yields optimal performance, minimizing ISE and IAE to 0.1155 and $7.365\times {10}^{-4}$, respectively. Furthermore, this optimal configuration attains an energy efficiency of 99.7%, notably outperforming the baseline three-function configuration, which exhibited a worst-case efficiency of 98.9 %. To assess robustness against dynamic environmental variations, this study subjects the optimal configuration to fluctuating irradiance and temperature profiles. Additionally, an analysis of computational resource consumption reveals that the proposed hybrid controller incurs a lower computational load for rule evaluation than three controllers reported in the recent literature. These findings demonstrate the system’s structural efficiency and superior optimization capability, achieving maximized photovoltaic energy harvesting at a low computational cost. Full article

The vertical landing of quadrotor unmanned aerial vehicles (UAVs) involves highly transient impact dynamics that generate significant vibrations on the UAV body, particularly under uncertain touchdown conditions such as uneven terrain, asymmetric ground contact, and high-impact landing. In this study, a robust semi-active vibration control framework is proposed for a quadrotor UAV equipped with a four-point soft landing gear system. The UAV is modeled as a three-degree-of-freedom rigid body including heave, pitch, and roll motions, while each landing gear leg is represented by an equivalent spring-damper mechanism with adaptively controllable damping characteristics. To evaluate the effectiveness of the proposed framework, PID (Proportional–Integral–Derivative), GA-PID (Genetic Algorithm-Based Proportional–Integral–Derivative), Fuzzy–PID (Fuzzy Logic-Based Proportional–Integral–Derivative), and ANFIS-PID (Adaptive Neuro-Fuzzy Inference System-Based Proportional–Integral–Derivative) controllers are comparatively investigated under five different landing scenarios. The nonlinear touchdown dynamics are implemented in the MATLAB/Simulink environment using a state-space-based simulation model. The results demonstrate that intelligent adaptive control methods significantly improve landing stability and vibration attenuation compared to the conventional PID controller. Among all methods, the ANFIS-PID controller achieved the best overall performance. Under the most severe landing condition, the peak vertical displacement was reduced from 0.114 m to 0.025 m, while the maximum pitch and roll angles decreased from approximately 11° to nearly 2°. Additionally, the settling time was reduced from nearly 10 s to below 3 s. Full article

In this article, a fuzzy payload compensation algorithm is proposed. In the context of simulating a machine vision model reconstruction, the target object is regarded as a cylinder to obtain the corresponding geometric size data. The first fuzzy mass prediction system is then used to predict the mass of the target object. During operation, real-time processing and calculation of the robotic arm’s joint motor current data are performed. Based on the mathematical relationship between the identified basic parameter set from the dynamic parameters and the end-effector payload, the second fuzzy compensation system was used to calculate the root mean square error (RMSE) of the predicted versus collected current data of the 6-th joint motor, thereby predicting and compensating for the payload mass. The final prediction is generated upon completion of the operation. The overall experiment is conducted on the HSR-CR607 robot. The experimental results indicated that the proposed prediction algorithm consistently operates within the acceptable error range (15%) in most test cases. Full article

High-frequency monitoring of manufacturing agility and Environmental, Social, and Governance (ESG) responsiveness is increasingly required in data-rich operations, yet many practical indices remain low-frequency, weakly decomposable, or difficult to interpret in weekly control settings. This study presents a single-enterprise methodological demonstration of a weekly fuzzy monitoring model with dual benchmarks for explainable operational control. The empirical panel covers 156 weeks from 2023 to 2025 across three plants, five product families, and 27 KPIs grouped into Operational Agility, Sustainable Responsiveness, and Socio-Market Adaptability. KPI and benchmark weights were elicited through a two-round Delphi process with 24 experts. The model combines fixed target-centered B1 compliance thresholds with percentile-calibrated B2 thresholds for direction-adjusted week-to-week adaptation. In the calibrated specification, the overall index mean is 0.618 with a range of 0.489 to 0.741, while the mean B1 and B2 values are 0.619 and 0.617. Matched-level validation at the plant–product–week level (N = 2340) shows a positive association with EBIT (Pearson r = 0.222, 95% CI [0.200, 0.245]), and time-safe calibration checks preserve the substantive interpretation of the index. The results support the model as an explainable, human-in-the-loop instrument for within-case weekly monitoring and diagnosis rather than as a broadly validated predictive model. Full article

Hybrid renewable energy systems (HRES) integrating photovoltaic arrays (PV), wind turbines (WT), and fuel cells (FC) require coordinated maximum power extraction to maintain stable operation under dynamic environmental and load conditions. Conventional MPPT approaches based on independent source-level control often suffer from adverse source interaction, increased steady-state oscillation, degraded DC-link stability, and reduced total extracted power when multiple renewable sources operate simultaneously. To address these limitations, this paper proposes an integrated perturb-and-observe control framework for coordinated power optimization in photovoltaic–wind–fuel-cell hybrid renewable energy systems connected through a shared DC-link structure. Unlike conventional independent MPPT controllers, the proposed strategy evaluates the aggregate power behavior of the integrated system and performs coordinated duty-cycle adaptation to improve renewable-energy utilization while suppressing source conflicts and dynamic coupling effects. The proposed controller is implemented and validated using a real-time digital simulator under a sequential disturbance profile consisting of an irradiance drop at 0.2 s, wind-speed increase at 0.4 s, hydrogen-pressure fluctuation at 0.6 s, and load variation at 0.8 s. Comparative evaluation against conventional perturb-and-observe, incremental conductance, and fuzzy-logic-based MPPT methods demonstrates that the proposed framework achieves a tracking efficiency of $97.8\%$, reduces steady-state tracking error to $2.2\%$, and improves settling time by $42.8\%$ under these dynamic operating conditions. In addition, the proposed controller exhibits lower oscillatory behavior, improved extracted renewable power, and enhanced DC-link stability during simultaneous multi-source disturbances. The results demonstrate that the proposed framework provides an effective real-time coordination strategy for hydrogen-enabled hybrid renewable energy systems operating under dynamically coupled renewable-source conditions. Full article

Understanding students’ vocational preferences in STEM domains is a complex challenge characterized by uncertainty, subjectivity, and overlapping interests. Traditional profiling approaches often rely on rigid categorizations that fail to capture the hybrid and dynamic nature of learners. This study proposes FAS-XAI, a reproducible learning analytics framework that integrates fuzzy logic and explainable artificial intelligence for interpretable profiling of STEM vocational preferences. The methodology combines fuzzy AHP for criterion weighting, Fuzzy C-Means clustering to identify overlapping profiles, and XGBoost for supervised validation, complemented by SHAP and LIME to provide global and local explanations of model behavior. The study is framed as a methodological simulation under controlled conditions, using synthetic data to evaluate the internal coherence, transparency, and transferability of the proposed pipeline. The results show that the framework can generate multidimensional and interpretable learner profiles, with resilience, communication, and commitment emerging as relevant discriminative dimensions within the simulated setting. Overall, the proposed approach provides a reproducible methodological basis for future empirical applications in personalized learning, vocational guidance, and AI-supported educational decision-making. Full article

Maximum power point tracking (MPPT) is a critical function for maximizing energy extraction in photovoltaic (PV) systems. Due to the inherently dynamic nature of the maximum power point under varying irradiance conditions, achieving fast convergence, low steady-state oscillations, and high tracking efficiency remains a challenging research problem. This paper proposes a hybrid ANN-based MPPT strategy for photovoltaic systems operating under rapidly changing environmental conditions. The proposed approach integrates a rule-based operating-condition estimation stage with a recurrent ANN-based control stage, enabling adaptive duty-cycle generation using measured PV voltage and current signals. Unlike conventional MPPT techniques, the proposed method utilizes operating-region estimation together with an extended ANN input feature vector and a recurrent backpropagation neural network to improve dynamic tracking performance under abrupt irradiance variations. In addition, a composite loss function is adopted to enhance tracking accuracy, guidance consistency, and control smoothness. The ANN is initially trained offline and subsequently refined online using lightweight incremental adaptation to maintain effective operation with a low computational burden. The proposed MPPT strategy is evaluated against P&O, FLC, and SMC. Simulation results demonstrate improved tracking performance, faster dynamic response, and reduced steady-state oscillations under abrupt irradiance variations. Full article

The concept of Elastic Patterns was originally proposed as a prototype-based classification approach that unifies perspectives from cognitive psychology, fuzzy logic, and physics. At their core, Elastic Patterns operate across two levels of deformation: a parameter-level deformation, quantified in terms of axial strain, and a pattern-level deformation, understood as the accumulation of deformation energy to perfectly fit the sample to be classified. This dual representation supports an interpretable and adaptive recognition mechanism, where classification emerges from selecting the Elastic Pattern that requires the minimal deformation energy to align with a real case to classify. This paper extends the theoretical and practical foundations of the proposed Elastic Patterns approach for adaptive pattern classification by introducing several deformation models, Spring Hardening, Weighted Spring Deformation, or Group Parameter Deformation to improve the capacity of Elastic Patterns to adapt to different contexts. These deformation models allow the proposal to adapt to different semantic contexts by controlling how parameter contraction and elongation are penalised. Additionally, novel adaptive mechanisms are introduced, which enable Elastic Patterns to dynamically adjust parameter relevance, capture inter-parameter dependencies, and better reflect contextual knowledge. Furthermore, the framework offers inherently interpretable classification via explicit parameter deformations and energies, avoiding post hoc explanations. Full article

Traffic signal control over urban networks requires coordinating the controllers of multiple signalized intersections toward a shared goal of minimizing network-wide congestion. Multi-agent reinforcement learning (MARL) methods have shown considerable promise in this setting. The epsilon–greedy exploration strategy adopted by many of these methods treats every candidate signal phase as equally worth trying, discarding the rich domain knowledge that traffic theory already provides. This paper proposes fuzzy-guided exploration, in which a multi-criteria fuzzy inference system uses local traffic conditions, with phase pressure as its primary input, to assign each candidate phase a priority. These priorities define a sampling distribution used in place of the uniform draw. We evaluate the method across four MARL algorithms covering independent learning (IQL) and the centralized training with decentralized execution paradigm (VDN, QMIX, and QPLEX) on both a synthetic grid and a real-world network. Fuzzy-guided exploration consistently improves upon the baseline in all combinations, with tangible gains on the synthetic grid and substantially larger improvements on the real-world network. These findings demonstrate that exploration is an effective intervention point for domain-knowledge integration in cooperative MARL, and that pressure-based scoring provides a well-suited signal to serve that role in traffic signal control. Full article