Search Results (3,825)

In precision aquaculture, feeding automation becomes particularly challenging when the dispenser operates on a non-fixed platform, as its dynamic behavior introduces perturbations that hinder accurate balance measurement and complicate dispenser control. To address this problem, this work proposes the integration of a weight estimator with robust control strategies. Two control approaches are evaluated: (i) a fuzzy proportional controller, where the fuzzy sets are generated using the fuzzy C-means clustering algorithm, and (ii) a self-tuning regulator (STR) based on based on an Autoregressive with Exogenous Input (ARX) model of the dispenser. In addition, the weight estimator employs a model of additive components dependent on the kinematics of the oscillating platform, with its hyperparameters experimentally optimized through cost function minimization. The proposal was experimentally validated using a compact prototype of an automatic dispenser mounted on an oscillating platform with pelletized feed, demonstrating robust performance and good dispensing accuracy, especially when employing the fuzzy-based control. Full article

Cost overrun is a major challenge in the construction industry. However, there is a notable lack of data from imperial studies that exhaustively identify and analyze risk factors contributing to overruns. This study aims to address this gap by systematically identifying and analyzing these risk factors. A hybrid methodology was employed. It combined a systematic literature review, a three-round Delphi process, and fuzzy set techniques. Insights from the literature review informed the first-round Delphi questionnaire. Subsequent rounds were refined based on earlier results. In the third round, experts’ opinions on the likelihood and impact of the cost risk factors were collected using a 5-point Likert scale. Finally, a fuzzy approach was employed to assess the severity of cost risk factors based on the combined effects of their likelihood and impact. The results revealed that the primary cost risk factors include escalation and fluctuation in material prices, inflation, material shortages, the country’s political instability, the country’s economic instability, delays in payment to the contractor, and delays in material procurement and delivery. Notably, the significant cost risk factors are largely beyond the contractor’s control and are closely tied to the broader political and economic conditions of the country. Full article

Despite extensive research into regenerative braking technology, balancing braking safety and energy recovery efficiency remains a challenge under complex and varied driving conditions. To address this, this paper proposes an adaptive fuzzy control strategy for the regenerative braking system in dual-drive electric motorcycles. Using braking intensity, vehicle speed, and battery state of charge (SOC) as inputs, the strategy employs fuzzy reasoning to dynamically adjust the regenerative braking force ratio in real-time. This approach maximizes energy recovery efficiency while ensuring braking safety. A co-simulation platform for the electromechanical hybrid braking system of the entire vehicle was built using MATLAB/Simulink and BikeSim. Compared with the conventional constant-regeneration scheme, the proposed adaptive fuzzy control strategy achieves a remarkable improvement in energy recuperation efficiency—25.97% under WMTC and 26.43% under FTP-75, respectively—while simultaneously increasing the terminal battery SOC by 2.1% and 1.3%. These quantitative gains substantiate the superior capability of the strategy to dynamically reconcile braking stability with energy-harvesting objectives across diverse driving conditions. By fully exploiting the regenerative potential of dual-drive architectures, the proposed control approach not only extends the achievable driving range but also provides a scalable framework for high-efficiency regenerative braking control in future lightweight electric vehicles. Full article

High-performance speed control of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicle (EV) applications faces significant challenges due to inherent nonlinearities, parameter variations, and signal non-idealities such as sensor noise and measurement latency. To address these issues, this paper proposes a robust PI-based Fractional-Order PSO-Fuzzy Weight Controller (PI-FOPSOFWC). The proposed strategy integrates a fractional-order PI (FOPI) core to ensure iso-damping robustness, a fuzzy inference mechanism for online gain scheduling against nonlinear load dynamics, and a novel Fractional-Order Particle Swarm Optimization (FOPSO) algorithm for optimal parameter tuning. A key contribution of this study is the validation of the control strategy within a high-fidelity co-simulation framework coupling MATLAB/Simulink with CarSim 2023, which incorporates realistic vehicle dynamics and time-varying road loads unavailable in conventional simplified simulations. Co-simulation results demonstrate that the proposed controller effectively eliminates overshoot in step responses and maintains stability under significant parameter mismatches ($2.0\times$ inertia). Furthermore, under the EPA urban driving cycle, the proposed method reduces the speed tracking Root Mean Square Error (RMSE) by 75.0% compared to the standard PI controller. Computational complexity analysis further confirms the feasibility of the proposed algorithm for real-time implementation in commercial EV traction drives. Full article

Through this paper, an active disturbance rejection control scheme is designed based on an extended state observer capable of estimating the system’s internal variables and external disturbances without the need for expensive sensors and also attenuates sensor-induced noise, supporting cleaner measurements. The extended state observer is dynamically adjusted using fuzzy logic techniques. The proposed method is validated in Matlab/Simulink, with the results showing a significant reduction in both body displacement and acceleration compared to passive suspension systems, representing a direct improvement in vehicle stability and ride comfort; this demonstrates the robustness and adaptability of the proposed system. The evaluation covers three road excitations, sinusoidal, step, and trapezoidal, to broaden the analysis under both smooth and abrupt disturbances. Full article

This paper presents a robust output-feedback fuzzy control scheme for autonomous bus–trailer systems, which can be formulated as a multi-input multi-output system. To ensure driving safety, the proposed design explicitly accounts for both input and output constraints. A core feature of this approach is the utilization of exponential dissipativity, which not only attenuates external disturbances but also serves as a unified framework encompassing exponential passivity and $H_{\infty }$ performance through the adjustment of weighting matrices. Additionally, a tunable decay rate is introduced to improve transient response characteristics. Recognizing that full state information is rarely available in practical scenarios, an observer is integrated to estimate unmeasurable state variables. Finally, the effectiveness and feasibility of the proposed control scheme are validated under various driving conditions through Simulink/dSPACE co-simulation. Full article

This study aimed to scientifically assess cold stress in dairy calves and optimize winter rearing protocols. A combined approach of feeding trials, expert surveys, and multidimensional data analysis was used to evaluate the effects of outdoor (−5~−28 °C) and indoor (5 °C) environments on Holstein dairy calves. A 60-day controlled trial was conducted with 20 healthy 5-day-old calves. In parallel, an interdisciplinary panel of 20 experts and 8 farmers established a cold stress evaluation system via the analytic hierarchy process (AHP), with cold stress levels quantified through fuzzy comprehensive evaluation. Environmental (weight = 0.62), physiological (weight = 0.22), and behavioral (weight = 0.16) factors contributed differentially to cold stress assessment, with data showing that outdoor calves were under mild cold stress (maximum membership degree = 0.64). The temperature–humidity index (THI) showed significant correlations with multiple physiological and biochemical parameters. Generalized linear mixed model (GLMM) analysis confirmed that THI variation significantly influenced calf standing time, respiratory rate (RR), malondialdehyde (MDA) content, and total antioxidant capacity (T-AOC). In feeding trials, indoor calves exhibited marginally higher average daily gain and body weight in early stages, whereas outdoor calves demonstrated significantly better growth performance by day 60. The outdoor group displayed increased lying and defecation behaviors, along with reduced locomotor/standing time and respiratory frequency. No significant intergroup differences were observed in serum immune or antioxidant indicators. Metabolomic analysis identified 20 differentially expressed metabolites, indicating an enhancement in the activity of energy metabolism pathways in calves. This study establishes a quantitative methodology for cold stress evaluation, clarifies environment–physiology–behavior interactions, and provides a theoretical basis for winter calf management. The results confirm that outdoor cold exposure did not hinder calf growth without compromising health, offering scientific support for optimizing outdoor rearing strategies in cold regions. Full article

This work addresses the design of a predefined-time adaptive fuzzy control scheme for high-order nonlinear systems with nonstrict-feedback structures, subject to unmodeled dynamics and input time delay. To mitigate the influence of unmodeled dynamics, a predefined-time auxiliary dynamic signal is incorporated into the controller design. Meanwhile, the adverse effects caused by input delay are handled by integrating a Padé approximation with the introduction of an intermediate state variable. Fuzzy logic systems are utilized to approximate the unknown nonlinear terms present in the system dynamics. Based on a recursive backstepping framework and a power-type Lyapunov function formulation, an adaptive fuzzy tracking controller with predefined-time convergence characteristics is constructed. A detailed stability analysis demonstrates that the closed-loop system achieves practical predefined-time convergence, while appropriate selection of design parameters guarantees that the tracking errors remain confined within a small bounded region around the origin. Finally, the effectiveness and advantages of the proposed control strategy are validated through a numerical example and a practical example. Full article

To address the uneven spatiotemporal distribution of electric vehicle (EV) charging demand and the high complexity of mobile charging vehicle (MCV) scheduling, this study proposes an integrated “prediction–pre-scheduling–real-time scheduling” solution. It focuses on optimizing the charging demand prediction model while refining the MCV scheduling strategy. First, a new red-billed blue magpie optimizer (NRBMO) is proposed. By integrating three improved strategies—initialization via a Circle chaotic map with opposition-based learning, adaptive Lévy flight search, and dynamic attack intensity adjustment—over the original red-billed blue magpie optimizer (RBMO), the NRBMO algorithm optimizes the membership function parameters of a fuzzy neural network (FNN), thus establishing the NRBMO-FNN charging demand prediction model. Second, MCV scheduling is implemented in phases based on the predictive results: during the pre-scheduling phase, macro-level vehicle allocation is achieved to minimize the total system cost; in the real-time scheduling phase, a multi-objective optimization model is constructed and integrated with a four-input, four-output adaptive fuzzy controller to realize the coordinated optimization of the total system cost, service time, and user inconvenience. Finally, the results demonstrate that under the G = 3 test set, the prediction accuracy of NRBMO-FNN outperformed other algorithms by at least 26.3%, 33.4%, and 6.6% in RMSE, MAE, and R², respectively. The proposed scheduling model reduced the three objective function values by an average of 3.41 yuan, 1.39 min, and 11.95 units during testing. Full article

The rapid growth of e-commerce, particularly in China, has led to a surge in express packaging waste, posing significant environmental challenges. However, consumer participation in express packaging recycling remains a critical yet underexplored issue. To address this gap, this study extends the Theory of Planned Behavior (TPB) by incorporating perceived benefit, perceived trust, and policy communication to explain consumer behavior. Survey data from 382 urban consumers in China were analyzed using an integrated approach combining partial least squares structural equation modeling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA), and necessary condition analysis (NCA). The results indicate that attitude, perceived benefit, and perceived trust significantly influence recycling behavior, while subjective norm, perceived behavioral control, and policy communication exhibit no significant net effects. Furthermore, configurational analysis demonstrates that high recycling behavior emerges from multiple combinations of factors rather than any single dominant driver, and NCA identifies attitude as a necessary prerequisite. In conclusion, these findings underscore that express packaging recycling is driven by complex interactions among benefits, trust, and attitudes, suggesting that policymakers should prioritize multi-factor policy designs to effectively promote sustainable consumer behavior. Full article

The brake-by-wire system is a fundamental and critical component of intelligent electric vehicles. Achieving precise actuator motor response is essential for brake-by-wire braking performance. To address this issue, this article proposes a fuzzy sliding-mode control method for a dual-motor electro-hydraulic braking system. An innovative model of the braking system is established, incorporating the motor, deceleration mechanism, brake master cylinder, brake wheel cylinder, and hydraulic system. Firstly, dynamic models for the permanent magnet synchronous motor (PMSM), the reduction mechanism, the brake master cylinder, and the brake wheel cylinder are developed. Subsequently, the feasibility of the redundant structure is verified. Finally, a novel composite convergence law-based fuzzy adaptive sliding mode control (SMC) method is designed. Simulation results demonstrate that this approach effectively reduces motor response time and enhances braking performance. Full article

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to address dynamic power variations. However, conventional proportional–integral (PI)-based control strategies for HESS can exhibit performance limitations under nonlinear and varying operating conditions. To overcome this drawback, this paper presents an adaptive neuro-fuzzy inference system (ANFIS)-based control strategy for HESS located in a DC microgrid, with comparative evaluation against both conventional PI and traditional Fuzzy Logic controller (FLC) schemes. The proposed approach is evaluated using a detailed MATLAB/Simulink R2024a model of a DC microgrid including EVs. Simulation results show that, under normal operating conditions, the ANFIS-based control demonstrates improved transient response, reduced voltage fluctuations, and effective coordination between the battery and supercapacitor during renewable power variations, compared to PI and FLC-controlled systems. In addition to nominal performance assessment, this work investigates the vulnerability of the ANFIS controller to cyber-attacks. Two representative attack scenarios, false data injection (FDI) and denial-of-service (DoS), are applied to critical measurement and control signals of HESS. Simulation results reveal that, although the DC-bus voltage regulation is largely maintained during attack intervals, cyber manipulation significantly disrupts the intended HESS power-sharing behavior. Full article

During a vehicle’s approach to a stop, significant longitudinal impact and pitch oscillations occur due to the decrease in vehicle speed and the substantial nonlinearity of the electro-hydraulic braking (EHB) system. To balance comfort and control accuracy at the end of braking, this paper proposes a comfort braking control strategy based on deceleration evolution characteristics. This method utilizes the adjustable pressure characteristics of the EHB system to construct an adaptive PI (proportional-integral) controller based on fuzzy rules, achieving a smooth transition between normal braking and comfort braking without mode switching. Simultaneously, target deceleration planning is introduced to gradually reduce the vehicle’s deceleration during the approach to a stop. Simulation and real-vehicle test results show that at initial speeds of 36 km/h, 40 km/h, and 44 km/h, the longitudinal deceleration impact amplitude is reduced by approximately 3.8%, 16.7%, and 11.7%, respectively. At 4 s, the vehicle pitch angle is reduced by 3.4%, 3.4%, and 3.8%, respectively. Meanwhile, the average braking distance change is less than 0.05%, and the maximum braking distance change is less than 0.1%. The results demonstrate that this strategy effectively improves braking comfort during the vehicle’s start-stop phase without compromising braking performance. Full article

This paper proposes the Fuzzy Difference Equation Matrix Model (FDEMM), a novel predictive control algorithm designed for nonlinear multivariable systems. Standard Dynamic Matrix Control (DMC) often struggles with computational load and nonlinearities. FDEMM addresses this by integrating the Difference Equation Matrix Model (DEMM) with a generalized Takagi-Sugeno (T-S) fuzzy framework, utilizing a parameter-weighting scheme to handle overlapping membership functions. The method is validated on two distinct nonlinear systems: a binary distillation column and a delayed thermal mixing tank. Results demonstrate FDEMM’s ability to control complex systems achieving the desired output even in the presence of disturbances and noise. The proposed strategy offers a computationally efficient alternative for real-time control of complex nonlinear processes. Full article

Natural gas separation and purification are critical stages for ensuring product quality, operational safety, and economic efficiency in the energy sector. However, a significant research gap exists: conventional control systems, predominantly based on a proportional-integral-derivative (PID) controller, are often static and lack the adaptability required to handle fluctuations in raw gas composition and operating conditions. This review aims to systematically analyze modern control strategies to identify the most influential parameters and effective methodologies for enhancing process efficiency. The methods involve a comparative assessment of classical PID control against advanced intelligent approaches, including adaptive control, fuzzy logic, and machine learning (ML) models, based on a synthesis of the recent literature and industrial case studies. The key finding is that data-driven and intelligent methods (e.g., neural networks, adaptive fuzzy controllers) demonstrate superior performance in achieving precise parameter adjustment, improving responsiveness, and optimizing energy consumption compared to traditional static systems. Such an integrated strategy transforms decision-making into a multivariable optimization framework with objectives encompassing minimizing pollutants, lowering energy usage, and enhancing end-product specifications. The present work argues for employing methodologies like systemic analyses and advanced computational techniques—particularly artificial neural networks—to forecast gas stream attributes. Full article