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Recent space missions demand higher pointing accuracy, smoother attitude transitions and lower energy consumption than those typically achievable with conventional control approaches. This motivates the exploration of intelligent and nonlinear control methods. The FuzzyBuzz experiment investigates the application of fuzzy logic for spacecraft attitude control using NASA’s Astrobee robotic system aboard the International Space Station. Unlike traditional control methods, fuzzy logic introduces a rule-based approach capable of handling uncertainties and nonlinearities inherent in space environments, making it particularly suited for autonomous operations in microgravity. The objective of FuzzyBuzz is to evaluate the effectiveness of fuzzy controllers compared to traditional linear ones, such as Proportional–Integral–Derivative (PID) and $H_{\infty }$ controllers. In addition, a comparison with a nonlinear controller based on a Model Predictive Control (MPC) strategy is considered. The controllers will be tested through predefined attitude maneuvers, evaluating precision, energy efficiency, and real-time adaptability. This work presents the design of the FuzzyBuzz experiment, including the software architecture, simulation environment, experiment protocol, and the development of a fuzzy logic-based attitude control system for Astrobee robots. The proposed fuzzy controller and a PID controller are optimized using a Multi-Objective Particle Swarm Optimization (MOPSO) method, providing a range of operational points with different trade-offs between two metrics, related to convergence time and energy consumption. Results show that the PID controller is better suited for scenarios demanding low convergence times, whereas the fuzzy controller provides smoother responses, reduced steady-state error, and maintains convergence under significant parametric uncertainties. Results from $H_{\infty }$ and MPC controllers will be reported once the in-orbit experiment is performed. Full article

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

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 Aerial Vehicle (UAV) swarms operating in contested environments face a critical “semantic gap” between raw, high-velocity network traffic and high-level mission security constraints, compounded by the risk of privacy leakage during collaborative learning. Existing deep learning (DL)-based Network Intrusion Detection Systems (NIDSs) suffer from opacity, prohibitive resource consumption, and vulnerability to gradient leakage attacks in federated settings, while traditional rule-based systems fail to handle encrypted payloads and evolving attack patterns. To bridge this gap, we present NeSySwarm-IDS (Neuro-Symbolic Swarm Intrusion Detection System), an end-to-end differentiable neuro-symbolic framework that simultaneously achieves high accuracy, strong privacy guarantees, and built-in interpretability under resource constraints. NeSySwarm-IDS integrates an extremely lightweight 1D convolutional neural network with a differentiable Łukasiewicz fuzzy logic reasoner incorporating attack-specific rules. By aggregating only low-dimensional logic rule weights with calibrated differential privacy noise, we drastically reduce communication overhead while providing $(ϵ,\delta )$-DP guarantees with negligible utility loss. Extensive experiments on the UAV-NIDD dataset and our self-collected dataset demonstrate that NeSySwarm-IDS achieves near-perfect detection accuracy, significantly outperforming traditional machine learning baselines despite using limited training data. A detailed case study on GPS spoofing confirms the interpretability of our approach, providing axiomatic explanations suitable for autonomous mission verification. These results establish that end-to-end neuro-symbolic learning can effectively bridge the semantic gap in UAV swarm security while ensuring privacy and interpretability, offering a practical pathway for deploying trustworthy AI in contested environments. Full article

This paper presents a comprehensive formulation of fuzzy path choice, based on representing utilities through fuzzy numbers. This approach advances the modelling of path choice problems in transportation systems. This model improves the ability to capture the uncertainty of travellers’ perceptions and behaviours, providing an alternative to traditional probabilistic frameworks. These models are the core of the assignment models used to simulate transport systems and calculate sustainability indicators. To support its use in assignment procedures, the paper set out the mathematical operations required for manipulating fuzzy quantities, ensuring internal consistency and operational feasibility. A key contribution is the combined use of normalised and non-normalised fuzzy numbers, which increases modelling flexibility and provides a novel way to simulate path overlap. The model is based on two approaches: the introduction of a factor that modifies the core of the fuzzy number, and an approach that modifies the confidence of the fuzzy number. The two approaches are specified and applied in a test network. Numerical applications demonstrate that the proposed method effectively accounts for path dependencies where traditional fuzzy operators fail. Full article

Risk-based management of water quality in drinking water supply systems requires decision-support tools that extend beyond parameter-level hazard assessment and enable prioritization at the level of physical system objects. In this context, hazard assessment refers specifically to drinking water quality parameters and their possible operational and health-related implications, particularly in facilities serving sensitive user groups. This study proposes a class-based extension of the FPOR (Fuzzy Priority of Objects at Risk) framework to support object-level operational prioritization under conditions of limited data availability. Hazard importance is adopted from prior hazard prioritization using the Fuzzy Priority Index (FPI), while priority premises (PP) are represented as object classes reflecting typical functional and operational characteristics. Class-based profiles of local hazard relevance and object vulnerability are defined using expert-informed fuzzy representations and aggregated into FPOR scores to produce a relative ranking of priority premises classes. The results demonstrate how hazard prioritization can be systematically propagated to object-level decision units without reliance on site-specific monitoring data. The proposed framework provides a transparent and scalable basis for early-stage risk-based planning and supports the operational implementation of object-oriented management strategies in drinking water systems, while maintaining a clear conceptual separation from health risk assessment addressed in subsequent studies. 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

This paper proposes a new fuzzy logic-based rotor flux model reference adaptive system (FLC-MRAS) for rotor speed estimation in induction motor drives, replacing the constant-gain PI controller used in conventional MRAS schemes. The proposed observer simultaneously incorporates both rotor flux and electromagnetic torque errors to enhance estimation accuracy and robustness against load torque disturbances. A nonlinear Mamdani-type fuzzy logic controller (FLC) with two inputs and one output, employing triangular membership functions and seven fuzzy sets, is adopted. The effectiveness and useful operational performance of the proposed approach is examined through extensive simulation cases under various vehicle speed driving profiles and load torque conditions using an indirect vector-controlled induction motor drive. In order to investigate the effective operational performance of a speed estimator, different cases are prepared according to the vehicle requirements. To examine the robustness of the proposed observer under realistic operating conditions, rotor resistance variation is incorporated into the simulation framework. This approach allows assessment of MRAS performance under practical nonlinearities and parameter uncertainties encountered in real applications. Comparative results demonstrate superior speed regulation and speed tracking, reduced estimation error, and faster convergence of the adaptive tuning signal for better speed estimation compared to the PI-MRAS observer. The proposed scheme provides the suitable choice of traction drive adoption for electric vehicle (EV) applications. Full article

The paper proposes an intelligent energy management system designed for a university laboratory room, centered on the user and based on the integration of smart sensors and fuzzy logic for the simultaneous optimization of thermal comfort and energy efficiency. The system architecture integrates three control methods, On/Off controller, Proportional Integral Derivative (PID) controller, and Fuzzy Logic, within a hybrid structure capable of managing multiple factors such as thermal comfort, energy consumption, and the availability of renewable energy sources. The system is implemented and tested using Zigbee 3.0 sensors, smart relays, and photovoltaic panels, while variables such as temperature, humidity, energy consumption, and user feedback are monitored. The simulation results, obtained in the MATLAB/Simulink development environment, demonstrate that the fuzzy algorithm reduces thermal oscillations, optimizes energy costs, and maintains perceived comfort within an optimal range. The main contribution of the study lies in the development of a user-centered, interpretable, and scalable architecture, along with a PowerApps application that records occupants’ feedback in real time, which can be implemented in smart buildings with limited computational resources. Two operating scenarios with different time periods were developed for the proposed system. The fuzzy controller maintained a mean temperature deviation below ±0.2 °C, reduced oscillatory behavior compared to PID controller, and enabled photovoltaic coverage of up to 29.97% during peak intervals, with an average daily contribution of 8.77%. The total simulated energy cost was 8.49 RON for the one-day scenario and 48.12 RON for the five-day interval. Full article

Sewerage systems are among the most fundamental and indispensable components of urban infrastructure. However, inadequate management can result in malfunctions and subsequent rehabilitation processes, leading to various negative consequences. Identifying areas at high risk of failure and conducting system-based inspections can significantly improve the performance of sewer networks. This study identified and categorized 33 criteria that could cause sewer system failures: structural, operational, hydraulic and environmental defects. A Bayesian network (BN) model was developed to determine dependencies between the criteria, quantify uncertainty, investigate new information about the structural condition of assets and calculate the effects and sensitivities of the criteria on the probability of failure. A probability-based risk assessment model was then created using a fuzzy inference system (FIS) to predict risk levels in sewerage systems under different combinations of physical and operational conditions and hydraulic and environmental effects. A case study was performed on a sewer network in Malatya, Turkey, determining its failure probability to be 76.6%, placing it in the high-risk category. When the probability of pipe failure was set to 100% in the Bayesian network model to evaluate the relative influence of different criteria, the most influential factors were identified as flow velocity (74.8%), clogging (71.4%), and failure rate (71.1%). Thanks to the flexible structure of BNs, the proposed model is expected to be useful for performing risk analyses in systems involving uncertainty or missing data. It can also be used to prioritize rehabilitation, inspection and maintenance programs, improve infrastructure service quality and ensure system reliability in urban sewerage systems. Full article

Segmenting non-rigid objects such as smoke in video requires effective utilization of temporal information, which remains challenging due to their irregular deformation and complex appearance variations. Based on our previously proposed DeffNet for industrial fumes video segmentation, this letter presents a novel adaptive frame selection algorithm that employs fuzzy logic control to dynamically optimize the temporal processing step size for the specific task of industrial smoke video segmentation. Our method quantifies inter-frame variation using the Structural Similarity Index (SSIM) and Normalized Cross-Correlation (NCC) as inputs to a fuzzy inference system. Gaussian membership functions, shaped via K-means clustering, and a five-rule fuzzy system are designed to determine the optimal step size, maximizing informative dynamic feature extraction while minimizing redundant computation. As a lightweight front-end module, the algorithm integrates seamlessly into the existing DeffNet segmentation framework without reconstructing new network architecture. Extensive experiments on a dedicated industrial smoke video dataset demonstrate that our approach effectively improves the segmentation performance of DeffNet, achieving 84.27% Intersection over Union (IoU) while maintaining a high inference speed of 39.71 FPS. This work provides an efficient and scene-specific solution for temporal modeling in industrial smoke non-rigid object segmentation and offers a practical improved strategy for DeffNet in real-time industrial smoke monitoring. Full article

Entrepreneurial ecosystems are multi-actor, uncertain, and dynamic environments in which policymakers and investors must balance innovation, resilience, and cost. Despite the growing literature on entrepreneurial ecosystems, much of the existing research has focused on identifying the components and relationships among actors and has provided less prescriptive frameworks for evaluating resource allocation policies before implementation. To address this gap, this study presents a digital twin-based and fuzzy multiobjective optimization framework for resource orchestration in entrepreneurial ecosystems. The proposed framework combines dynamic ecosystem representation with multiobjective decision-making under uncertainty and allows for the testing of different resource allocation and policy scenarios before actual intervention. To solve the model, exact optimization in GAMS was used for small- and medium-sized samples, and NSGA-II and ACO algorithms were used for large-scale problems. The advantage of the proposed method is that, unlike purely descriptive approaches or deterministic models, it simultaneously considers uncertainty, time dynamics, and trade-offs between innovation, resilience, and cost in an integrated decision-making framework. Experimental evaluation was conducted based on simulated data calibrated with reliable public sources, and the performance of the algorithms was compared with reference methods in terms of computational time, solution quality, and stability. The results showed that metaheuristics, especially NSGA-II, significantly reduced the solution time in large-scale problems and at the same time produced solutions closer to the Pareto frontier and with greater stability. Sensitivity analysis also showed that in the designed scenarios, policy budgets have a more prominent effect on innovation, while resource capacity and structural diversification play a more important role in enhancing resilience. Also, improving resource efficiency has had the greatest effect on reducing the total system cost. From a theoretical perspective, the present study operationally models the logic of resource orchestration in entrepreneurial ecosystems through the integration of digital twins and fuzzy multi-objective optimization. From a managerial perspective, this framework acts as a decision-making engine that allows for ex ante testing of policies, clarification of trade-offs, and extraction of resource allocation rules under uncertainty. Full article

The Internet of things (IoT) is rapidly pervading daily life and linking everything. Although higher connectivity offers many benefits, including higher productivity, robotic processes, and decision-making guided by data, it also poses a number of security dangers. Modern risks to data authenticity and confidence are getting harder to handle through typical central safety solutions. In this paper, we present a detailed investigation of the latest innovations and approaches for assessing reputation and confidence in the blockchain-empowered Internet of Things (BIoT) area. A comprehensive literature search was conducted across major electronic databases, including IEEE, Springer, Elsevier, Wiley, MDPI, and top indexed conference proceedings. The publication year was restricted to the period from 2018 to 2025. The methodological quality of a total of 122 studies met the inclusion criteria assessed using predefined quality measures. We figure out existing flaws at each layer of IoT architecture, illustrating how autonomous, transparent, and impenetrable blockchain ledgers address these flaws. Plus, we analytically compare public, private, consortium, and hybrid blockchain networking architectures to emphasize the underlying compromises among security, reliability, and decentralization. We also assess how reputation evaluation techniques evolved over time, moving from classical fuzzy logic and weighted average models to modern mature game theory and machine learning (ML) models, addressing their limitations in terms of computational overhead, scalability, adaptability, and deployment feasibility in IoT systems. Additionally, we outline future directions for BIoT system trust assessment and identify research limitations and potential solutions. Our research indicates that although ML-driven models offer more accurate predictions for identifying illicit node activities, they are still constrained by limited unbalanced data and high processing overhead. Full article

This paper investigates the consensus problem for a class of uncertain nonlinear multi-agent systems (MASs) subject to external disturbances with unknown control directions (UCDs). A novel control scheme integrating Nussbaum-type gain is proposed to actively compensate for UCDs, while fuzzy logic systems (FLSs) are embedded in a feed-forward compensator to approximate unknown nonlinear dynamics, thereby achieving global stability. The proposed distributed control laws ensure global asymptotic convergence for both first- and second-order MASs through Lyapunov stability analysis. By implementing a strategic reparameterization technique, this scheme systematically reduces computational complexity, requiring each agent to adapt only a minimal parameter set. Moreover, the framework is extended to address complex formation control tasks. Comprehensive simulations validate the efficacy of the theoretical findings. Full article