Search Results (212)

The rapid development of artificial intelligence (AI) has created new opportunities for improving the performance, robustness, and efficiency of electrical machine drive systems. Among AI-based approaches, fuzzy logic control (FLC) has attracted considerable attention because of its ability to handle nonlinear dynamics, parameter uncertainties, and external disturbances without relying on an accurate mathematical model. This review systematically examines FLC-based control strategies for electrical machine drives, with particular emphasis on induction motors, switched reluctance motors, permanent-magnet synchronous motors, synchronous reluctance motors, and brushless DC motors. The review follows the PRISMA 2020 framework, and the selected studies are analyzed according to machine type, FLC architecture, control strategy, optimization method, implementation platform, and validation approach. The reviewed evidence indicates that FLC-based strategies can improve dynamic response, tracking accuracy, robustness, and torque regulation under the specific conditions reported in the literature. Hybrid approaches combining FLC with field-oriented control, direct torque control, sliding-mode control, model predictive control, neural networks, ANFIS, and optimization algorithms provide additional opportunities for adaptation and parameter tuning. However, the reported performance is strongly dependent on machine topology, controller architecture, tuning methodology, computational requirements, and validation platform. The review also identifies important limitations, including the lack of standardized benchmarking, computational complexity, dependence on expert knowledge, and limited HIL and experimental validation of several advanced approaches. Emerging directions include Type-2 and higher-order fuzzy systems, neuro-fuzzy and hybrid AI controllers, data-driven optimization, digital-twin-assisted control, edge computing, and hardware-oriented implementation. The objective of this review is to provide a structured and critical synthesis of the existing evidence, clarify the evolution and practical applicability of AI-driven FLC approaches, and identify research priorities for reliable, computationally efficient, and experimentally validated electrical machine control. Full article

Agricultural drip fertigation is a highly coupled dynamic process in which precision resource management depends on the coordinated performance of the entire equipment chain. Against the backdrop of global water scarcity and excessive fertilizer application, improving the full-process precision of mixing, injection, sensing, control, distribution, and terminal delivery has become a prerequisite for the wider adoption of fertigation. This review evaluates advanced process-monitoring technologies and closed-loop control architectures within modern cyber-physical fertigation systems, covering fertilizer solution preparation and mixing, injection devices, liquid- and solid-phase state sensing, intelligent control algorithms, and pipeline distribution with terminal emitters. Online mixing has evolved from gravity-based batch pre-mixing toward continuous metered injection with vortex-guided static mixing, electrical conductivity (EC) sensing with drift compensation and granular mass flow detection form the perceptual basis of closed-loop regulation, control has advanced from proportional–integral–derivative (PID) controllers through variable-universe fuzzy logic to artificial neural network (ANN) hybrids with metaheuristic optimization, and pipeline pressure regulation together with emitter anti-clogging strategies determine long-term distribution uniformity. A quantitative analysis shows that the attainable precision of the sensing–decision–execution chain is bounded by the coupling among sensor accuracy, process delays, control performance, and actuator response rather than by any single device. The review identifies five unresolved gaps—sensor reliability, multi-season field validation, interoperability, low-cost automation, and fertilizer-type adaptability—and recommends that future research prioritize low-cost Internet of Things (IoT) sensor arrays on low-power wide-area networks, edge–cloud collaborative control, and foundation-model-driven autonomous decision-making, co-designed as one coupled specification. Full article

This paper studies the asymptotic synchronization problem for drive-response delayed fuzzy bidirectional associative memory (BAM) neural networks via a frequency-domain approach. The network model explicitly retains fuzzy logic operations—fuzzy AND and fuzzy OR—in its connection weights, as originally proposed in the literature. Two linear feedback controllers are designed: Controller I uses only instantaneous error feedback, while Controller II additionally incorporates a delayed error term to compensate for transmission delays. By transforming the closed-loop error dynamics into a Lur’e-type system, two novel frequency-domain synchronization criteria are derived from the multivariable circle criterion and Parseval’s theorem. The proofs are fully self-contained and detailed, using signal energy arguments to rigorously handle truncation and initial conditions without constructing Lyapunov–Krasovskii functionals. The criteria only require evaluating the minimum eigenvalue of a frequency-dependent Hermitian matrix over a bounded interval. Numerical simulations confirm the theoretical results and show that the delayed-feedback controller significantly enlarges the synchronizable region. The potential applicability of the proposed framework to associative memory, secure communication, and cooperative control is also discussed. Full article

Aiming at the difficulty of extracting fault features from nonlinear and non-stationary vibration signals of planetary gearboxes, a planetary gearbox fault diagnosis method based on Refined Composite Multiscale Fuzzy Entropy (RCMFE), Parametric-t-distributed Stochastic Neighbor Embedding (P-t-SNE), and Artificial Jellyfish Search Algorithm Optimized Support Vector Machine (JS-SVM) is proposed. Firstly, RCMFE is used to calculate and combine the feature vectors of the original fault signals of the planetary gearbox to construct the original high-dimensional fault feature set. Secondly, Parametric-t-SNE (P-t-SNE) based on a deep feedforward neural network is employed to reduce the dimensionality of the high-dimensional features, thereby extracting sensitive low-dimensional features and achieving out-of-sample mapping. Finally, the low-dimensional features are inputted into the JS-SVM for the identification of fault types. The experimental results of planetary gearbox fault diagnosis show that the proposed method can accurately identify common faults in planetary gearboxes, demonstrating promising application prospects. Full article

This survey presents a large-scale, reproducible, and parameter-mapped benchmarking analysis of path planning algorithms for multiple mobile robot systems (MMRS) by systematically examining 247 rigorously filtered papers from high-impact journals. Unlike prior reviews that primarily provide conceptual taxonomies, this survey introduces execution-oriented multi-parameter mapping enabling direct comparison of classical planners (A*, D*, Cell Decomposition, APF, RM, RRT, and ORCA), nature-inspired metaheuristics (PSO, GA, ACO, GWO, FA, ABC, BFO, CS, BA, SFLA, eagle-inspired optimizers), and learning-driven AI frameworks (Fuzzy Logic, Artificial Neural Networks, and Deep Reinforcement Learning). Each paper is evaluated across 15 practical planning dimensions, including environment type (static 95% vs. dynamic 51%), multi-robot validation (52%), dynamic goal handling (13%), energy awareness (14%), timepath optimization bias (82% focus), inter-robot coordination (less than 47%), and software validation platforms (MATLAB 42% and ROS 9%), revealing that simulation-only validation dominates (98%) while experimental testing remains limited (33%). Multivariate validation through Multiple Correspondence Analysis further confirms that coordination maturity, energy awareness, and multi-robot applicability are the primary structural differentiators of deployment readiness across algorithm families. The findings emphasize the need for hybrid, energy-aware, and coordination-driven MRPP frameworks supported by experimental benchmarking and reproducible deployment pipelines to advance real-world MMRS autonomy. Full article

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

Fuzzy logic-based mineral potential mapping was applied to the Tisová–Klingenthal Cu–Co VMS deposit (Erzgebirge) in the Czech–German border region. The study area is characterized by heterogeneous geological and geochemical datasets derived from differing national surveys and historical mining. Using the Exploration Information System (EIS) toolkit, a knowledge-driven fuzzy logic approach integrated key spatial datasets, including copper and zinc soil and stream sediment anomalies and metabasalt lithology, relevant to Besshi-type VMS deposits. Three prospective anomalies were identified: the historic Tisová mine and two additional targets aligned along the same stratigraphic horizon. Artificial Neural Network (ANN) modelling was limited by insufficient training data, resulting in overfitting and reduced predictive reliability. Follow-up soil geochemical surveys conducted over the largest anomaly returned locally elevated copper values but did not conclusively confirm mineralisation. The results demonstrate that fuzzy logic provides a flexible and interpretable framework for mineral potential mapping in complex, data-scarce environments and highlight the need for iterative modelling and targeted exploration. Full article

Due to its fast start and stop, purity, and reliability, hydropower is becoming more important in the overall power dispatch strategy in grids with a high proportion of wind and solar power generation. Therefore, we propose an interval type-2 fuzzy logic-based rainfall classification and fuzzy neural network model to build a 48 h reservoir inflow forecasting system, addressing the challenges of renewable energy instability and extreme weather in hydropower operations. Full article

This systematic literature review analyzes the role of dynamic difficulty adaptation (DDA) in serious games (SGs) to provide an overview of current trends and identify research gaps. The purpose of the study is to contextualize how DDA is being employed in SGs to enhance their learning outcomes, effectiveness, and game enjoyment. The review included studies published over the past five years that implemented specific DDA methods within SGs. Publications were identified through Google Scholar (searched up to 10 November 2025) and screened for relevance, resulting in 75 relevant papers. No formal risk-of-bias assessment was conducted. These studies were analyzed by publication year, source, application domain, DDA type, and effectiveness. The results indicate a growing interest in adaptive SGs across domains, including rehabilitation and education, with DDA methods ranging from rule-based (e.g., fuzzy logic) and player modeling (using performance, physiological, or emotional metrics) to various machine learning techniques (reinforcement learning, genetic algorithms, neural networks). Newly emerging trends, such as the integration of generative artificial intelligence for DDA, were also identified. Evidence suggests that DDA can enhance learning outcomes and game experience, although study differences, limited evaluation metrics, and unexplored opportunities for adaptive SGs highlight the need for further research. Full article

This paper develops a mathematically grounded neuro-fuzzy control framework for IoT-enabled irrigation systems in precision agriculture. A discrete-time, physically motivated model of soil moisture is formulated to capture the nonlinear water dynamics driven by evapotranspiration, irrigation, and drainage in the crop root zone. A Mamdani-type fuzzy controller is designed to approximate the optimal irrigation strategy, and an equivalent Takagi–Sugeno (TS) representation is derived, enabling a rigorous stability analysis based on Input-to-State Stability (ISS) theory and Linear Matrix Inequalities (LMIs). Online parameter estimation is performed using a Recursive Least Squares (RLS) algorithm applied to real IoT field data collected from a drip-irrigated orchard. To enhance prediction accuracy and long-term adaptability, the fuzzy controller is augmented with lightweight artificial neural network (ANN) modules for evapotranspiration estimation and slow adaptation of membership-function parameters. This work provides one of the first mathematically certified neuro-fuzzy irrigation controllers integrating ANN-based estimation with Input-to-State Stability (ISS) and LMI-based stability guarantees. Under mild Lipschitz continuity and boundedness assumptions, the resulting neuro-fuzzy closed-loop system is proven to be uniformly ultimately bounded. Experimental validation in an operational IoT setup demonstrates accurate soil-moisture regulation, with a tracking error below 2%, and approximately 28% reduction in water consumption compared to fixed-schedule irrigation. The proposed framework is validated on a real IoT deployment and positioned relative to existing intelligent irrigation approaches. Full article

Reliable assessment of bridge technical condition is a key challenge in infrastructure management due to uncertainty, subjectivity, and heterogeneity inherent in inspection-based data. Traditional deterministic evaluation methods often fail to capture the gradual nature of structural deterioration and the complex interactions between bridge components. This study proposes a hybrid methodology that integrates fuzzy logic and artificial neural networks (ANNs) to quantify the overall technical condition of bridge structures using long-term inspection data. A comprehensive dataset, derived from real bridge inspection reports collected over more than 15 years across various regions of Ukraine, served as the basis for model development. Five key input parameters—substructure condition, superstructure condition, deck condition, overall structural condition, and channel and channel protection condition—were employed to compute an integrated Bridge Condition Assessment indicator using a Mamdani-type fuzzy inference system. The resulting fuzzy-based indicator was subsequently used as the target variable for training ANN models. To ensure optimal predictive performance and training stability, Bayesian Optimization was applied for systematic hyperparameter tuning. Model performance was evaluated using standard regression metrics, including MSE, MAE, MAPE, and the coefficient of determination (R²). The results demonstrate that the proposed approach enables accurate approximation of the fuzzy-based Bridge Condition Assessment indicator, with MAPE values as low as 0.2% and R² exceeding 0.982 for the best-performing model. The hybrid framework effectively combines interpretability and scalability, providing a decision-support framework based on fuzzy logic and surrogate modeling for automated fuzzy-based bridge condition assessment, maintenance prioritization, and integration into digital asset management systems. Full article

Fuzzy Cognitive Maps (FCMs) are a type of recurrent neural network with built-in meaning in their architecture, originally devoted to modeling and scenario simulation tasks. These knowledge-based neural systems support feedback loops that handle static and temporal data. Over the last decade, there has been a noticeable increase in the number of contributions dedicated to developing FCM-based models and algorithms for structured pattern classification and time series forecasting. These models are attractive since they have proven competitive compared to black boxes while providing highly desirable interpretability features. Equally important are the theoretical studies that have significantly advanced our understanding of the convergence behavior and approximation capabilities of FCM-based models. These studies can challenge individuals who are not experts in Mathematics or Computer Science. As a result, we can occasionally find flawed FCM studies that fail to benefit from the theoretical progress experienced by the field. To address all these challenges, this survey paper aims to cover relevant theoretical and algorithmic advances in the field, while providing clear interpretations and practical pointers for both practitioners and researchers. Additionally, we will survey existing tools and software implementations, highlighting their strengths and limitations towards developing FCM-based solutions. Full article

This paper proposes a Takagi–Sugeno–Kang Elliptic Type-2 Fuzzy Brain-Imitated Neural Network (TET2FNN)-based decoupling control strategy for nonlinear underactuated mechanical systems subject to uncertainties. A sliding-mode framework is employed to construct a decoupled control architecture, in which an intermediate variable is introduced to separate two second-order sliding surfaces, thereby forming a decoupled slip surface. The TET2FNN acts as the main controller and approximates the ideal control law online, while a robust compensator is incorporated to suppress approximation errors and guarantee closed-loop stability. Simulation studies conducted on a double inverted pendulum system demonstrate that the proposed method achieves improved tracking accuracy and disturbance rejection compared with representative state-of-the-art controllers. Furthermore, the computational burden remains reasonable, indicating that the proposed scheme is suitable for real-time implementation and practical nonlinear control applications. Full article

Accurate classification of wafer map defect patterns is crucial for enhancing yield in semiconductor manufacturing. To address the problem of deep learning model over-fitting to label noise present in real industrial data, this study proposes a fuzzy logic-based framework for identifying both single and composite-type defect patterns. To demonstrate the robustness of our approach, we utilized the public dataset WM-811K and developed a Fuzzy Inference System (FIS) that leverages quantitative metrics such as the Center Zone Density (CZD). Data quality was also improved through preprocessing steps, including resolving class imbalances and refining labels via expert review. The performance of the proposed FIS was evaluated against a quantitative feature-based neural network, an FIS-neural network hybrid, and a CNN model. Experimental results showed that in single-pattern classification, the proposed FIS model achieved the highest accuracy of 99.20%, followed by the feature-based neural network (91.63%), the FIS-neural network hybrid model (88.55%), and the CNN (81.06%). These results prove that the proposed FIS approach maintains high classification accuracy while offering the advantages of interpretability and rule-based adjustability. This framework presents a practical solution that can effectively integrate domain knowledge to reduce the risk of overfitting in data environments with imperfect labels. Full article

Currently, factors such as geopolitical conflicts, frequent extreme weather events, and power struggles among major countries are threatening the stability of the global supply chain. Building a more resilient supply chain has received international consensus. Today, new quality productivity (NQP), spawned by disruptive innovation, is an important way for China to enhance its agricultural product supply chain resilience (SCR). However, studies often overlook the “time lag” problem of the panel data adopted, and their empowering paths require further investigation. Therefore, this study firstly constructs NQP and agricultural product SCR indicators. Based on the panel data produced by 31 Chinese provinces from 2011 to 2022, we solved the “time lag” problem by integrating a Backpropagation Neural Network (BPNN) with an Autoregressive Integrated Moving Average (ARIMA) model to predict the NQP level. Subsequently, the empowering paths through NQP-enhancing agricultural product SCR were explored via entropy weight TOPSIS and Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method. Foundations: China’s agricultural product SCR exhibits a spatial differentiation characteristic of “prominent in the central region and weak in the western region”. A single factor is not a necessary condition for high resilience, and its improvement depends on the synergy of multiple factors. Three differentiated driving paths have been identified: “autonomous endogenous driving type”, “environment-enabled driving type”, and “system architecture driving type”. NQMP has become the bottleneck for improving agricultural product SCR, and the threshold of each factor has increased significantly as the resilience target is raised. High resilience stems from the synergy and functional compensation of core factors, while low resilience is mostly caused by the concurrent absence of key conditions or structural mismatch, showing distinct “multiple concurrencies” and “causal asymmetry” characteristics. Full article