Search Results (1,023)

In the realm of sustainable public transportation, the integration of intelligent electric bus propulsion systems represents a novel and promising approach to reducing environmental impact—particularly through the mitigation of NOx emissions and overall exhaust pollutants. This emerging technology underscores the growing need for advanced drive control architectures that ensure not only operational safety and reliability but also compliance with increasingly stringent emissions standards. The present article introduces an innovative analysis of energy-optimized dual-drive electric propulsion systems, with a specific focus on their potential for real-world application in emission-conscious urban mobility. A detailed dynamic model of a dual-drive electric bus was developed in MATLAB Simulink, incorporating a Fuzzy Logic-based decision-making algorithm embedded within the Transmission Control Unit (TCU). The proposed control architecture includes a torque-limiting safety strategy designed to prevent motor overspeed conditions, thereby enhancing both efficiency and mechanical integrity. Furthermore, the system architecture enables supervisory override of the Fuzzy Inference System (FIS) during critical scenarios, such as gear-shifting transitions, allowing adaptive control refinement. The study addresses the unique control and coordination challenges inherent in dual-drive systems, particularly in relation to optimizing gear selection for reduced energy consumption and emissions. Key areas of investigation include maximizing efficiency along the motor torque–speed characteristic, maintaining vehicular dynamic stability, and minimizing thermally induced performance degradation. The thermal modeling approach is grounded in integral formulations capturing major loss contributors including copper, iron, and mechanical losses while also evaluating convective heat transfer mechanisms to improve cooling effectiveness. These insights confirm that advanced thermal management is not only vital for performance optimization but also plays a central role in supporting long-term strategies for emission reduction and clean, efficient public transportation. Full article

To address the limitation that single-index evaluation fails to fully reflect the development performance of reservoirs of different types and at various development stages, a multi-index comprehensive evaluation system featuring the workflow of “index screening–weight determination–model evaluation–strategy guidance” was established. Firstly, the grey correlation analysis method (with a correlation degree threshold set at 0.65) was employed to screen 12 key evaluation indicators, including reservoir physical properties (porosity, permeability) and development dynamics (recovery factor, water cut, well activation rate). Subsequently, the fuzzy analytic hierarchy process (FAHP, for subjective weighting, with the consistency ratio (CR) of expert judgments < 0.1) was coupled with the attribute measurement method (for objective weighting, with information entropy redundancy < 5%) to determine the indicator weights, thereby balancing the influences of subjective experience and objective data. Finally, two evaluation models, namely the fuzzy comprehensive decision-making method and the unascertained measurement method, were constructed to conduct evaluations on 308 reservoirs in the Liaohe Oilfield (covering five major categories: integral medium–high-permeability reservoirs, complex fault-block reservoirs, low-permeability reservoirs, special lithology reservoirs, and thermal recovery heavy oil reservoirs). The results indicate that there are 147 high-efficiency reservoirs categorized as Class I and Class II in total. Although these reservoirs account for 47.7% of the total number, they control 71% of the geological reserves (154,548 × 10⁴ t) and 78% of the annual oil production (738.2 × 10⁴ t) in the oilfield, with an average well activation rate of 65.4% and an average recovery factor of 28.9. Significant quantitative differences are observed in the development characteristics of different reservoir types: Integral medium–high-permeability reservoirs achieve an average recovery factor of 37.6% and an average well activation rate of 74.1% by virtue of their excellent physical properties (permeability mostly > 100 mD), with Block Jin 16 (recovery factor: 56.9%, well activation rate: 86.1%) serving as a typical example. Complex fault-block reservoirs exhibit optimal performance at the stage of “recovery degree > 70%, water cut ≥ 90%”, where 65.6% of the blocks are classified as Class I, and the recovery factor of blocks with a “good” rating (42.3%) is 1.8 times that of blocks with a “poor” rating (23.5%). For low-permeability reservoirs, blocks with a rating below medium grade account for 68% of the geological reserves (8403.2 × 10⁴ t), with an average well activation rate of 64.9%. Specifically, Block Le 208 (permeability < 10 mD) has an annual oil production of only 0.83 × 10⁴ t. Special lithology reservoirs show polarized development performance, as Block Shugu 1 (recovery factor: 32.0%) and Biantai Buried Hill (recovery factor: 20.4%) exhibit significantly different development effects due to variations in fracture–vug development. Among thermal recovery heavy oil reservoirs, ultra-heavy oil reservoirs (e.g., Block Du 84 Guantao, with a recovery factor of 63.1% and a well activation rate of 92%) are developed efficiently via steam flooding, while extra-heavy oil reservoirs (e.g., Block Leng 42, with a recovery factor of 19.6% and a well activation rate of 30%) are constrained by reservoir heterogeneity. This system refines the quantitative classification boundaries for four development levels of water-flooded reservoirs (e.g., for Class I reservoirs in the high water cut stage, the recovery factor is ≥35% and the water cut is ≥90%), as well as the evaluation criteria for different stages (steam huff and puff, steam flooding) of thermal recovery heavy oil reservoirs. It realizes the transition from traditional single-index qualitative evaluation to multi-index quantitative evaluation, and the consistency between the evaluation results and the on-site development adjustment plans reaches 88%, which provides a scientific basis for formulating development strategies for the Liaohe Oilfield and other similar oilfields. Full article

The integration of ultracapacitors into the propulsion systems and implicitly into the hybrid energy storage systems (HESSs) of electric vehicles offers significant prospects for increasing performance, improving efficiency and extending the lifetime of battery systems. However, the realization of these benefits critically depends on the implementation of sophisticated control algorithms. From fundamental rule-based systems to advanced predictive and intelligent control strategies, the evolution and integration of these algorithms are driven by the need to efficiently manage the power flow, optimize energy utilization and ensure the long-term reliability of hybrid energy storage systems. This study briefly presents (in the form of a mini review) the research in this field and the development directions and application of state-of-the-art control algorithms, also highlighting the needs, challenges and future development directions. Based on the analysis made, it is found that from the point of view of performance vs. ease of implementation and computational resource requirements, fuzzy algorithms are the most suitable for HESS control in the case of common applications. However, when the performance requirements of HESSs relate to special and high-tech applications, HESS control will be achieved by using convolutional neural networks. As electric vehicles continue to evolve, the development of more intelligent, adaptive and robust control algorithms will be essential for achieving the full potential of integrating ultracapacitors into electric mobility. Full article

This paper proposes an event-driven dynamic output feedback dissipative fuzzy (EDDOFDF) control strategy for direct current (DC) microgrids with nonlinear constant power loads (CPLs) subject to hybrid attacks and noises. Firstly, using the measurement output of the microgrid’s fuzzy model and information of hybrid attacks, a Zeno-free resilient event-triggered communication mechanism (RETM) is designed, which can save limited resources such as network bandwidth and actively exclude attack-induced packet dropouts. Secondly, by designing an EDDOFDF security controller, a closed-loop switched fuzzy system model is established, which presents a unified platform to study the impacts of hybrid attacks, RETM, noises, microgrid plant, and controllers. Thirdly, by introducing a piecewise Lyapunov functional, exponential stability conditions in mean square with guaranteed dissipative performance are obtained. Further, sufficient conditions for designing both the EDDOFDF controller and state-feedback switched fuzzy controller are derived. Examples illustrate the effectiveness of the proposed method. Full article

Reinforcement corrosion significantly reduces the load-bearing capacity, ductility, and energy dissipation of reinforced concrete (RC) structures, thereby increasing their seismic failure risk. To enhance the seismic performance of in-service RC structures, this study employs an FRP mesh–engineered cementitious composite (ECC) retrofitting method and develops a multi-objective optimization decision-making framework. A finite element model incorporating reinforcing steel corrosion, concrete deterioration, and bond–slip effects is first established and validated against experimental results. Based on this model, a six-story RC frame is selected as a case study, and eight alternative FRP mesh–ECC retrofitting schemes are designed. Five core indicators are quantified, namely annual collapse probability, expected annual loss, capital expenditure, carbon emissions, and downtime. The results indicate that FRP mesh–ECC retrofitting can significantly improve the seismic performance of corroded RC structures. The overall uniform retrofitting scheme (SCS-2) achieves the most significant improvements in seismic safety and economic performance, but they are associated with highest capital expenditure and carbon emission. Story-differentiated schemes (SCS-3 to SCS-6) provide a trade-off between performance enhancement and cost–emission control. While partial component-focused schemes (SCS-7 and SCS-8) cut cost and carbon but do not lower seismic downtime. Furthermore, the improved fuzzy-TOPSIS method with interval weights and Monte Carlo simulation indicates that the balanced scheme SCS-1 delivers the most robust performance across five dimensions, with a best probability close to 90%. The results confirm the potential of FRP mesh–ECC retrofitting at both component and structural levels and provide a practical reference for selecting seismic retrofitting strategies for existing RC structures. Full article

Enhancing ultra-capacitor (UC) utilization and mitigating battery stress are pivotal for improving the energy management efficiency and service life of hybrid energy storage systems (HESSs). Conventional energy management strategies (EMSs), however, rely on fixed parameters and therefore struggle to allocate power flexibly or reduce battery degradation. This paper proposes a VMD-LSTM-based EMS that incorporates auto-tuning weight and constraint to address these limitations. First, a VMD-LSTM predictor was proposed to improve the velocity and road gradient prediction accuracy, thus leading an accurate power demand for EMS and enabling real-time parameter adaptation, especially in the nonlinear area. Second, the model predictive controller (MPC) was adopted to construct the EMS by solving a multi-objective problem using quadratic programming. Third, a combination of rule-based and fuzzy logic-based strategies was introduced to adjust the weights and constraints, optimizing UC utilization while alleviating the burden on batteries. Simulation results show that the proposed scheme boosts UC utilization by 10.98% and extends battery life by 19.75% compared to traditional MPC. These gains underscore the practical viability of intelligent, optimizing EMSs for HESSs. Full article

The control of infectious diseases plays a critical role in safeguarding the health of species and ecosystems. In this study, we investigate the combined effects of prey refuge and harvesting as mechanisms to limit the spread of disease within predator populations. A deterministic model is developed to examine the system dynamics through local stability analysis of equilibria, and the framework is further extended to an uncertain setting via a fuzzified model. The analysis shows that for small refuge values, the system reaches a stable state where infected predators move toward extinction, while prey and susceptible predators exhibit strong oscillations. As the refuge increases, the system undergoes a Hopf bifurcation, transitioning from periodic oscillations to a stable interior equilibrium. Beyond a critical threshold, oscillations disappear entirely. Harvesting of susceptible predators reveals that moderate harvesting induces oscillatory behavior in both prey and susceptible predator populations, whereas excessive harvesting can drive both predator classes to extinction. Harvesting of infected predators, by contrast, consistently drives their extinction regardless of harvesting intensity, with the other populations maintaining oscillatory patterns. These results indicate that an appropriate combination of prey refuge and harvesting can serve as an effective strategy for disease control in predator populations. Full article

This research presents the development and characterization of a semi-automatic electrophoretic deposition (EPD) system designed for the synthesis of zinc oxide (ZnO) microstructures, utilizing a bioinspired neuro-fuzzy control strategy (ANFIS). The system was designed based on a chemical reactor regulated by electricity in a potentiostate cell to automate and optimize the deposition parameters by controlling the temperature. The synthesized ZnO coatings exhibited distinctive flake-like morphology, confirmed via Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), and Energy-Dispersive X-Ray Spectroscopy (EDS), validating their morphological uniformity and compositional consistency. The implemented ANFIS controller was trained using experimentally acquired data, making a correlation with the properties of the sample, thickness and porosity, also employed as inputs of the system. The system exhibited high accuracy in predicting optimal deposition conditions for ZnO nanoflakes obtention, specifically in the temperature-dependent variations in thickness and porosity employed as reference to establish four classes of working sets based on the density of ZnO flakes in the substrate. Results indicate that the bioinspired neuro-fuzzy control substantially enhances the adaptability and predictive capabilities of the electrophoretic deposition process, making it a versatile tool suitable for various applications requiring precise microstructural characteristics. Future directions include further refinement of the control system, incorporation of digital sensing technologies, and potential expansion of the platform to accommodate other functional materials and complex deposition scenarios. Full article

Enhancing ride comfort and vibration control performance is a critical requirement for fuel cell commercial vehicles (FCCVs). This study develops a semi-active seat suspension control strategy that integrates a fuzzy logic controller with a Modified Fireworks Algorithm (MFWA) to systematically optimize fuzzy parameters. A seven-degree-of-freedom (7-DOF) half-vehicle model, including the magnetorheological damper (MRD)-based seat suspension system, is established in MATLAB/Simulink to evaluate the methodology under both random and bump road excitations. In addition, a hardware-in-the-loop (HIL) experimental validation was conducted, confirming the real-time feasibility and effectiveness of the proposed controller. Comparative simulations are conducted against passive suspension (comprising elastic and damping elements) and conventional PID control. Results show that the proposed MFWA-FL approach significantly improves ride comfort, reducing vertical acceleration of the human body by up to 49.29% and seat suspension dynamic deflection by 12.50% under C-Class road excitation compared with the passive system. Under bump excitations, vertical acceleration is reduced by 43.03% and suspension deflection by 11.76%. These improvements effectively suppress vertical vibrations, minimize the risk of suspension bottoming, and highlight the potential of intelligent optimization-based control for enhancing FCCV reliability and passenger comfort. Full article

This study examines the landing performance of a four-legged lunar lander equipped with magnetorheological dampers when landing on discrete lunar soil. To capture the complex interaction between the lander and the soil, a coupled dynamic model is developed that integrates flexible multibody dynamics (FMBD), granular material modeling, and a semi-active fuzzy control strategy. The flexible structures of the lander are described using the floating frame of reference, while the lunar soil behavior is simulated using the discrete element method (DEM). A fuzzy controller is designed to achieve the adaptive MR damping force under varying landing conditions. The FMBD and DEM modules are coupled through a serial staggered approach to ensure stable and accurate data exchange between the two systems. The proposed model is validated through a lander impact experiment, demonstrating good agreement with experimental results. Based on the validated model, the influence of discrete lunar regolith properties on MR damping performance is analyzed. The results show that the MR-based landing leg system can effectively absorb impact energy and adapt well to the uneven, granular lunar surface. Full article

This study proposes a novel sensorless maximum power capture control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to enable adaptive learning capabilities. Additionally, support vector regression (SVR) is employed to estimate wind speed without the use of mechanical sensors, thereby enhancing system reliability and reducing maintenance requirements. A vanadium redox battery (VRB) is integrated to enhance power stability under fluctuating wind conditions. Simulation results demonstrate that the proposed FPNN-IPSO-based controller achieves superior performance compared to conventional Takagi–Sugeno–Kang (TSK) fuzzy and proportional–integral (PI) controllers. Specifically, the FPNN-IPSO controller exhibits notable improvements in average power output, tracking accuracy, and overall system efficiency. The proposed method increases power output by 9.71% over the PI controller and supports Plug-and-Play operation, making it suitable for intelligent microgrid integration. This work demonstrates an effective approach for intelligent, sensorless MPC control in hybrid wind–battery microgrids. Full article

This paper proposes a novel finite-time adaptive fuzzy control strategy for two-stage continuous stirred tank reactor (CSTR) systems. The method integrates the gradient descent (GD) algorithm with Armijo line search to dynamically adjust the learning rate, thereby optimizing the parameters of fuzzy logic systems (FLSs) for fast and accurate approximation of unknown nonlinear functions. The proposed control scheme, based on finite-time stability theory, ensures convergence of system states to the desired trajectory within finite time. Compared with conventional adaptive fuzzy control methods, the approach effectively addresses the issues of slow convergence and low approximation accuracy, significantly reducing approximation error while enhancing convergence performance. Simulation results on a two-stage CSTR system verify that the proposed controller achieves rapid convergence and high approximation accuracy. Full article

This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered communication mechanism (DSTCM) with a prescribed-time control strategy. Furthermore, a fuzzy control strategy is designed to effectively suppress system disturbances, enhancing the robustness of the formation. The designed DSTCM not only retains the adaptive triggering threshold characteristic of dynamic event-triggered communication, significantly reducing communication frequency, but also completely eliminates the need for continuous state monitoring required by traditional event-triggered mechanisms. As a result, both communication and onboard computational resources are effectively conserved. In parallel, a novel time-varying unilateral constrained performance function is introduced to construct a prescribed-time controller, which guarantees that the formation tracking error converges to a predefined residual set within a user-specified time. The convergence process is independent of initial conditions and strictly adheres to full-state constraints. A rigorous Lyapunov-based stability analysis demonstrates that all signals in the closed-loop UAV velocity and attitude system are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the proposed DSTCM ensures the existence of a strictly positive lower bound on the inter-event triggering intervals of the UAVs, thereby avoiding the occurrence of Zeno behavior. Numerical simulation results are provided to verify the effectiveness and superiority of the proposed control scheme. Full article

Ensuring reliable power system control demands innovative architectural solutions. This research introduces a fault-tolerant hybrid parallel compensator architecture for load frequency control (LFC), combining a Proportional–Integral–Derivative with Filter (PIDF) compensator with a Fuzzy Fractional-Order PI-PD (Fuzzy FOPI–FOPD) module. Particle Swarm Optimization (PSO) determines optimal PID gains, while the Catch Fish Optimization Algorithm (CFOA) tunes the Fuzzy FOPI–FOPD parameters—both minimizing the Integral Time Absolute Error (ITAE) index. The parallel compensator structure guarantees continuous operation during subsystem faults, substantially boosting grid reliability. Rigorous partial failure tests confirm uncompromised performance-controlled degradation. Benchmark comparisons against contemporary controllers reveal the proposed architecture’s superiority, quantifiable through transient metric enhancements: undershoot suppression (−9.57 × 10⁻⁵ p.u. to −1.17 × 10⁻⁷ p.u.), settling time improvement (8.8000 s to 3.1511 s), and ITAE reduction (0.0007891 to 0.0000001608), verifying precision and stability gains. Resilience analyses across parameter drift and step load scenarios, simulated in MATLAB/Simulink, demonstrate superior disturbance attenuation and operational stability. These outcomes confirm the solution’s robustness, dependability, and field readiness. Overall, this study introduces a transformative LFC strategy with high practical viability for modern power networks. Full article

With the increasing demand for sustainable food production, the facility agriculture is progressively developing towards automation and intelligence. Traditional control techniques such as PID, fuzzy logic, and model predictive control have been widely applied in greenhouse planting for years. Existing greenhouse management systems still face challenges such as limited adaptability to fluctuating outdoor climates, and difficulties in maintaining both productivity and cost-effectiveness. Recently, with the development of greenhouse systems towards comprehensive environmental perception and intelligent decision-making, a large number of intelligent control and modeling technologies have provided new opportunities for the technological update of greenhouse management systems. This review systematically summarizes recent progress in greenhouse regulation and crop growth control technologies, emphasizing applications of intelligent techniques, involving adaptive strategies, neural networks, and reinforcement learning. Special attention is given to how these methods improve system robustness and control performance in terms of environmental stability, crop productivity, and energy efficiency, which are key performance indicators of greenhouse systems. Their advantages over conventional strategies in agricultural greenhouse systems are also analyzed in detail. Furthermore, the integration of intelligent technologies with greenhouse system modeling is examined, covering both greenhouse environmental models and crop growth models. The strengths and weaknesses of different techniques, such as mechanism, computational fluid dynamics (CFD), and data-driven models, are analyzed and discussed in terms of accuracy, computational cost, and applicability. Finally, future challenges and research opportunities are discussed, emphasizing the need for real-time adaptability, sustainability, and cluster intelligence. Full article