Search Results (1,580)

To improve the path-following performance of autonomous underwater vehicles (AUVs) under ocean currents, a control method based on line-of-sight with fuzzy controller (FLOS) guidance and the fuzzy sliding mode controller (FSMC) is proposed. This method considers multiple factors affecting guidance and adaptively determines the optimal heading angle through the fuzzy controller to enhance guidance capability. Additionally, a novel FSMC based on Lyapunov stability theory is designed to suppress the influence of model uncertainty and external disturbances on the control system. Simulations and experiments of the proposed control method demonstrate that it can maintain precise tracking under disturbances, improving path-following performance metrics by more than 15%. Full article

In order to clearly determine the carbon emission distribution of regions, lines or nodes in the power grid, this paper applies cloud theory to the evaluation of the distribution of carbon emissions in the power grid. Based on the theory of carbon emission flow in the whole life cycle, five indicators that can reflect the spatial and temporal distribution of carbon emissions are constructed from the two dimensions of space and time. Cloud theory is used to establish the standard cloud of the carbon emission distribution level to quantify the randomness and fuzziness of the data to be evaluated. The bilateral constraint cloud theory and data-driven cloud transformation are combined to construct five comprehensive standard clouds of excellent, good, medium, poor and inferior, which are used as the evaluation interval of the evaluation index of carbon emission distribution. The reverse cloud is used to convert multiple sets of data into cloud droplets. Through the similarity measurement algorithm based on cloud model overlap, the comprehensive evaluation level of carbon emission distribution state in the time dimension is determined. Taking the IEEE 39 system as the research object, the spatial and temporal distribution of carbon emissions is evaluated, and the rationality and effectiveness of the proposed model are verified. Finally, the influence of the new energy penetration rate and power supply structure on the carbon emission distribution of the power grid is discussed by using cloud computing. Based on this, the targeted carbon reduction strategies for different types of nodes and the method of measuring the optimal new energy penetration rate are proposed and can provide a decision-making reference for optimizing the carbon emissions of the power grid. Full article

Industry 4.0 supply chains, characterized by dynamic environments, uncertainty, and intricate interdependencies, necessitate robust decision-making tools. While existing models have made strides in addressing these complexities, they often struggle to effectively handle the high degree of uncertainty inherent in such systems. To bridge this gap, this research introduces a novel framework grounded in the axioms of Cubic Type-2 Fuzzy Soft Sets (CT2FSSs). By leveraging the enhanced flexibility and uncertainty-handling capabilities of CT2FSSs, our proposed framework empowers decision-makers to navigate complexities, optimize supply chain processes, and mitigate risks while maintaining symmetry in decision-making. Through rigorous theoretical analysis and practical applications, this study not only advances fuzzy set theory but also demonstrates its efficacy in the context of Industry 4.0. The unique contribution of this research lies in the development of a CT2FSS-based framework that offers superior adaptability to uncertain and complex environments, thereby enhancing the resilience and performance of supply chains in symmetrical scenarios. Full article

This paper presents a control strategy for a 4Y octocopter aircraft that is influenced by multiple actuator faults and external disturbances. The approach relies on a disturbance observer, adaptive type-2 fuzzy sliding mode control scheme, and type-1 fuzzy inference system. The proposed control approach is distinct from other tactics for controlling unmanned aerial vehicles because it can simultaneously compensate for actuator faults and external disturbances. The suggested control technique incorporates adaptive control parameters in both continuous and discontinuous control components. This enables the production of appropriate control signals to manage actuator faults and parametric uncertainties without relying only on the robust discontinuous control approach of sliding mode control. Additionally, a type-1 fuzzy logic system is used to build a fuzzy hitting control law to eliminate the occurrence of chattering phenomena on the integral sliding mode control. In addition, in order to keep the discontinuous control gain in sliding mode control at a small value, a nonlinear disturbance observer is constructed and integrated to mitigate the influence of external disturbances. Moreover, stability analysis of the proposed control method using Lyapunov theory showcases its potential to uphold system tracking performance and minimize tracking errors under specified conditions. The simulation results demonstrate that the proposed control strategy can significantly reduce the chattering effect and provide accurate trajectory tracking in the presence of actuator faults. Furthermore, the efficacy of the recommended control strategy is shown by comparative simulation results of 4Y octocopter under different failing and uncertain settings. Full article

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments. Full article

This article examines the potential of fuzzy set theory for analysing gradual changes in land use patterns within peri-urban areas. The primary objective of the study was to propose a methodology based on fuzzy set theory for the precise delineation of city boundaries and the identification and spatial localisation of the urban–rural transition zone. The analysis focused on elucidating the defining parameters of this area and the scope of land use changes within the urban–rural transition zone. The analysis employed data from four discrete time points. The data were collected in 2005, 2010, 2017, and 2022. The characteristics of the urban–rural transition zone were evaluated through an examination of historical data and the current land use patterns in regions experiencing direct urbanization pressure. The study demonstrated that, although spatial barriers remain, the city’s development has continued at a consistent pace. Between 2005 and 2010, the area of land classified as urban exhibited a 10% increase, with a further 7% increase observed in the subsequent period, spanning 2010 to 2017. In the most recent period under examination, the urban land area increased by 9%, a figure that is consistent with the rates observed in previous years. These results indicate the stability of urbanization processes in the analysed city, while also revealing significant changes in the limits of urban development and in the intensity of land use. The research project concentrated on the city of Olsztyn and the neighbouring suburban areas, which are subject to direct influence from the city’s expansion. The area under study encompasses 202.4 km² within an eight-km radius of the city centre. The authors of the study emphasized the necessity for systematic monitoring of changes in the transition zone between urban and rural areas. This is to ensure effective control of spatial development and ongoing adjustment of planning tools to effectively prevent uncontrolled expansion. The methodology used enabled the precise delimitation of urban development and the transition zone. This allowed for an in-depth analysis of changes in land use intensity. Full article

In the construction of prefabricated buildings, safety issues occur frequently, posing challenges to project progress and personnel safety. As a new trend in the construction industry, the complexity of the environment in prefabricated construction demands an update to traditional safety management concepts. This study introduces the concept of resilience to analyze safety issues in prefabricated construction and develops a WSR-4Rs framework for a systematic evaluation of construction safety. The study first combines the WSR (Wuli-Shili-Renli) systematic methodology with the 4R resilience theory to construct an evaluation index system for construction safety. Then, it uses the Analytic Hierarchy Process (AHP) and the entropy weight method to determine the combined weights of each index, establishing a balanced and objective weighting scheme. A fuzzy comprehensive evaluation model is then applied to assess actual project cases. Finally, an obstacle degree model is introduced to identify key indicator factors that significantly impact construction safety, and specific improvement measures are proposed based on these findings. The aim is to provide practical references and guidance for enhancing the safety management level in prefabricated construction. Full article

This article investigates the issue of $H_{\infty }$ security output feedback control for a nonlinear networked cascade control system with deception attacks. First, to further reduce the amount of communication data, reasonably schedule network resources, and alleviate the impact of multi-channel deception attacks, an attack-dependent adaptive event-triggered mechanism is introduced into the primary network channel, and its adaptive triggered threshold can be adjusted according to the random attack probability. Secondly, the output dynamic quantization of the secondary network channel is considered. Then, a novel security cascade output feedback controller design framework based on the Takagi–Sugeno (T-S) fuzzy networked cascade control system under deception attacks is established. In addition, by introducing the Lyapunov–Krasovskii stability theory, the design conditions of the controller are given. Finally, the effectiveness and superiority of the proposed design strategies are verified by two simulation examples of power plant boiler–turbine system and power plant boiler power generation control system. Full article

The risk of both fatal accidents and non-fatal injuries in the construction industry is significantly high in most countries. To reduce this construction safety risk, the proper use of personal protective equipment (PPE) is one of the major measures on the jobsite. In this research, in order to comprehensively assess the PPE plans, a three-phase framework was proposed to identify the optimal solution for PPE planning from a set of alternatives. As a result, four main criteria and fifteen sub-criteria were identified based on a systematic literature review, and a decision-making model integrating the analytic network process (ANP) and VIekriterijumsko KOmpromisno Rangiranje was developed. As the assessment information in the survey was incomplete and vague, the fuzzy sets theory was adopted to transform the linguistic terms into fuzzy numbers for evaluation. The model further calculated the weight of each criterion and prioritized the potential PPE plan alternatives. Finally, the presented model was implemented in a case study to verify its feasibility and applicability for practical construction management. The proposed method enables the selection of the most compromising solution as the optimal PPE plan. This research assists decision-makers and safety planners at construction workplaces to improve the overall safety performance and reduce accident risks, which significantly contributes to construction safety management and practice. Full article

Based on the analysis of the influencing factors of the current planning and utilization of river and lake shorelines, an index system of the current planning and utilization of river and lake shorelines is constructed. Based on the triangular fuzzy number method and the improved CRITIC method, the evaluation indicators are weighted and analyzed and then integrated to obtain the combined weight. An improved matter-element extension model was constructed to evaluate the current situation of shoreline planning and utilization in typical regions. At the same time, the TOPSIS evaluation model and the cloud theory evaluation model were compared and analyzed to verify the accuracy of the model. The improved matter-element model accounts for indicator incompatibility and the uncertainty of evaluation boundaries, offering a clearer reflection of the planning and utilization status. Full article

The design of the gear quantity and transmission parameters of a vehicle has large effects on its economical and power performance. This paper mainly researches the gear conditions (including the gear quantity and each gear’s transmission parameters) of two-gear and three-gear AMT (Automated Mechanical Transmission). This research uses Cruise software to build a multi-gear simulation model of a BEV (Battery Electric Vehicle) and adopts the LHS (Latin hypercube sampling) method to design an experiment plan and conduct a simulation experiment. This paper proposes a systematic method for influencing factor analyses and the optimization of transmission parameters, combining fuzzy theory, multiple regression, and particle swarm optimization. The research results show that the gear quantity allowing for optimal overall performance is three. The highest score obtained in the results of the simulation experiment for three-gear AMT is 11.15% higher than that of the two-gear AMT. The optimal design plan for the two-gear AMT is a small ig₁ with a big k₁, in which case the highest score of the regression model increases by 2.67% compared with that before modeling. The optimal design plan for the three-gear AMT is a big k₁ with a big k₂, in which case the highest score of the regression model increases by 12.78% compared with that before modeling. Then, this research uses PSO (particle swarm optimization) to further optimize the regression models and compares the difference between the highest scores in the results of the simulation experiment. The difference between the highest scores of the three-gear and two-gear AMT further increases to 21.95% after optimization. As shown in the results, the key factor influencing the performance of two-gear and three-gear AMT is gear quantity. Full article

A distributed improved robust integral line-of-sight (RILOS) guidance-based sliding mode controller is designed for multiple underactuated autonomous surface vessels (ASVs) to perform cooperative maritime search operations. First, a parallel circle search pattern is designed based on the detection range of ASVs, which can provide the reference formation shape. Second, an improved RILOS method is presented by introducing an integral term into the improved robust LOS method, which can counteract the disadvantageous effect of the unknown sideslip angle and kinematic discrepancy simultaneously. Third, distributed improved RILOS guidance is presented by integrating the extended second-order consensus algorithm into the improved RILOS method; then, the desired heading angle and desired velocity are generated for the control system simultaneously. Finally, the fuzzy logic system is integrated into the sliding mode control (SMC) method to approximate the unknown nonlinear function; then, a distributed improved RILOS guidance-based SMC controller is presented for multiple ASVs. The closed-loop signals are proved to be stable by the Lyapunov theory. The effectiveness of the presented method is verified by multiple simulations. Full article

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit. Full article

Unlike existing memristive neural networks or fuzzy neural networks, this article investigates a class of Caputo fractional-order uncertain memristive neural networks (CFUMNNs) with fuzzy operators and transmission delay to realistically model complex environments. Especially, the fuzzy symbol AND and the fuzzy symbol OR as well as nonlinear activation behaviors are all concerned in the generalized master-slave networks. Based on the characteristics of the neural networks being studied, we have designed distinctive information feedback control protocols including three different functional sub-modules. Combining comparative theorems, inequality techniques, and stability theory, novel delay-independent conditions can be derived to ensure the finite-time synchronization (FTS) of fuzzy CFUMNNs. Besides, the upper bound of the settling time can be effectively evaluated based on feedback coefficients and control parameters, which makes the achievements of this study more practical for engineering applications such as signal encryption and secure communications. Ultimately, simulation experiments show the feasibility of the derived results. Full article

In light of the current dearth of strip intermittent fertilization devices in standardized orchards, this study presents the design of an intermittent fertilization control system based on fruit tree crown detection, developed with the objective of meeting the agronomic requirements of strip furrow fertilization in standardized orchards. The initial stage of the process entails the design of the essential components of the fertilization apparatus, followed by the construction of the intermittent fertilization control system. The ultrasonic sensor was employed as the fruit tree crown detection module, and a mathematical model of fertilization speed was constructed to achieve uniform intermittent fertilization. Furthermore, in order to enhance the responsiveness and reliability of the fertilization servo motor, MATLAB Simulink was employed to assess the dynamic performance of the system under disparate control strategies. Ultimately, to validate the simulation outcomes, a field trial was conducted to assess the precision and uniformity of intermittent fertilization. The results demonstrate that the dynamic performance of the system under the fuzzy PID control strategy is optimal, and the coefficient of variation of the fertilizer uniformity of the intermittent fertilization device is less than 7%. The mean effective fertilization rate exceeded 85%, with the primary indices satisfying the agronomic criteria. Full article