Search Results (518)

Bioenergy with carbon capture, utilization, and storage (BECCUS) is increasingly regarded as a potential pathway to achieve net-negative emissions and support deep decarbonization. This review examines BECCUS from the perspective of circular carbon systems by integrating biomass resources, bioenergy conversion, carbon capture technologies, storage and utilization pathways, economic feasibility, sustainability challenges, and policy requirements. To improve conceptual clarity, the review distinguishes among BECCS, BECCU, and BECCUS based on carbon retention time, lifecycle boundaries, and final product fate. Recent advances in carbon capture technologies, including absorption, adsorption, membrane separation, chemical looping, cryogenic separation, direct air capture, and hybrid systems, are reviewed with particular attention to biomass-based applications. Major utilization and storage pathways are also critically assessed, including geological storage, CO₂-enhanced recovery, cement mineralization, CO₂-to-chemicals, CO₂-to-fuels, microalgae-based conversion, and agricultural CO₂ utilization. These pathways are compared in terms of technology readiness, mitigation potential, permanence, energy demand, economic feasibility, and major limitations. A bibliometric analysis is further included to identify research hotspots and emerging trends. Overall, BECCUS is a promising but highly pathway-dependent option whose climate benefits depend on sustainable biomass supply, lifecycle emissions, low-carbon hydrogen, infrastructure availability, and consistent carbon accounting. Full article

The advancement of sensor technology has spurred the emergence of data-driven fault detection (FD) strategies. Particularly for many control systems with time-varying parameter characteristics, data-driven FD offers significant practical value and application value. This paper addresses the FD problem for linear time-varying (LTV) systems. Specifically, within the Sieve estimation framework, the stable kernel representation of LTV systems is designed using a novel data stacking method and B-spline basis functions. Subsequently, the coefficient function is constructed to provide a general solution to online FD tasks. Finally, this method is validated through experiments. The results demonstrate that the sieve estimation-based strategy exhibits excellent estimation and FD performance in both offline and online phases. Full article

The LC-filtered permanent magnet synchronous motor (PMSM) drive system presents challenges due to its high-order characteristics, as well as the phase delay and voltage drop introduced between the inverter side and the motor side. These issues make traditional sensorless control methods difficult to apply directly. To address this, this paper proposes a sensorless control strategy based on a second-order generalized integrator (SOGI)-assisted high-order extended state observer (HOESO). This strategy relies solely on DC bus voltage and inverter-side currents to realize precise observation of the motor-side current and back electromotive force (BEMF), thereby significantly reducing system cost. Furthermore, the method enables gain parameter tuning merely by adjusting the observer bandwidth, and it also demonstrates that the system is stable when the estimated current is used to drive the capacitor current feedback active damping (CCFAD). In addition, the voltage differential components are extracted using the SOGI, effectively suppressing high-frequency noise interference. Experimental results obtained on an LC-filtered PMSM platform show that, under a 0.8 N·m step load at 600 rpm and 1200 rpm, the maximum motor-side current estimation errors are 0.66 A and 1.24 A, respectively, and the steady-state rotor position estimation errors are kept within 10°. Compared with direct differentiation, the SOGI-based differential extraction reduces the maximum BEMF estimation errors from 20.16 V and 47.54 V to 10.08 V and 12.96 V, respectively. Full article

Valve-controlled asymmetric hydraulic cylinders inherently exhibit bidirectional dynamic asymmetry attributable to differential chamber areas and heterogeneous gravitational coupling. This study proposes an event-triggered asymmetric gain RBF-PID strategy, wherein real-time directional identification enables differentiated gain scheduling between extension and retraction strokes to compensate for direction-dependent dynamic discrepancies inherent to asymmetric actuators. A sparse RBF mechanism with heterogeneous event-triggering thresholds is further introduced to achieve synergistic coordination between adaptive compensation and computational lightweighting. Uniform ultimate boundedness of the closed-loop tracking errors is rigorously established via Lyapunov-based stability analysis. Simulation results demonstrate that steady-state errors of the three joints are constrained within ±1°; compared with standard PID, the root-mean-square error is reduced for all joints, with directional switching overshoot suppressed below 2%. Relative to conventional RBF-PID, the proposed strategy achieves an event-triggering rate below 5% while reducing FLOPs by approximately 86%, effectively reconciling the inherent conflict between tracking accuracy and computational burden. Full article

Modern consumer technologies have become more intuitive and user-friendly. On the other hand, as automation levels rise in most factories and warehouses, it is becoming increasingly difficult to configure certain processes without a specialist’s involvement. Typically, these facilities employ staff without an engineering background in robotics, who can only perform their direct duties but are unable to create maps and routes for mobile robots. This article describes an alternative approach to creating a multi-purpose navigation map using mixed reality glasses, demonstrating the capabilities of Meta’s Meta Quest 3 glasses as a versatile mixed reality device for 3D room scanning and navigation mapping. In this paper, we demonstrate how a mixed reality approach can be applied to additional mapping evaluation for rooms of varying complexity and shape, including problematic areas for many similar solutions when the room contains mirrors and glass walls. Although environmental scanning in the Meta Quest 3 glasses is a standard integrated function of these glasses, it is used only for the collision avoidance system; this device can be successfully implemented in the Internet of Things system, and the collected data can be used for 3D scanning of the environment, creating navigation maps after additional data processing. This method expands the implementation of automated moving systems, including simplified map creation for mobile robots, in complex environmental and movement tasks. Full article

Synchronous reluctance motors in quadruped robot joint drives are prone to active-current peaks, abrupt rate variations, and switching-neighborhood error concentration under foot–ground impacts and obstacle-induced load steps, leading to prediction lag and peak underestimation. To address these issues, this paper proposes a dynamic-response-enhanced multi-condition active-current prediction method based on TPE-VMD-BiLSTM-TRC. First, the original sequence is segmented according to operating-condition boundaries, and prediction samples are constructed within each segment to reduce cross-condition information leakage and distribution inconsistency. Second, variational mode decomposition is performed on each segmented sequence to separate multi-scale fluctuation components, and BiLSTM is used for mode-wise one-step prediction. Third, TPE jointly optimizes the VMD decomposition parameters and BiLSTM hyperparameters to improve parameter matching under different operating conditions. Furthermore, a switching-aware composite loss and a switch-gated residual correction branch are introduced to enhance dynamic tracking and compensate for structural bias in switching neighborhoods. Experiments on variable-frequency drive data show that, compared with TPE-VMD-BiLSTM, the proposed method reduces the overall RMSE, switching-neighborhood RMSE, and MAPE by approximately 29.8%, 38.8%, and 7.4%, respectively. On the additional held-out operating-condition segment, the overall RMSE is reduced by 28.8%, indicating stable prediction performance across different segments. Full article

Plunger lift systems are vital for optimizing production in gas wells, but their performance can be compromised by various operational anomalies. Traditional diagnostic methods and conventional convolutional neural network (CNN) approaches often struggle with the complex, transient data from these systems, particularly in capturing long-range temporal dependencies and generalizing from limited, imbalanced datasets. This study presents an enhanced diagnostic framework for plunger lift anomaly detection by leveraging the strengths of a pre-trained Vision Transformer (ViT). The methodology transforms one-dimensional time-series pressure data into two-dimensional image representations using the element-wise summation of Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF), which simultaneously preserves global operational trends and local transient dynamics for vision model analysis. The ViT model, initialized with pre-trained weights, is further optimized using Bayesian optimization (BO) for hyperparameter tuning, and a tailored data augmentation pipeline is employed to improve robustness. Comparative evaluations demonstrate that the proposed ViT-based approach, particularly the ViT + GAF + BO model, significantly outperforms baseline CNN models and their optimized variants, achieving the highest Precision, Recall, and F1-score, with an F1-score of 0.93. Visualizations using t-SNE confirm the ViT’s superior capability in learning discriminative features, showcasing well-separated clusters for different operational conditions compared to CNNs. This research underscores the potential of pre-trained ViTs combined with appropriate data representation and optimization techniques for achieving accurate and reliable anomaly detection in plunger lift systems. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

To address the degraded prediction accuracy, increased torque ripple, and weakened dynamic response of conventional finite-control-set model predictive control (FCS-MPC) under magnetic saturation, parameter mismatch, and load disturbances in permanent magnet synchronous motors (PMSMs), this paper proposes a hierarchical FCS-MPC framework based on PINN-RLS and virtual-voltage-vector extension, termed HRPV-MPC. Built upon a unified nonlinear motor model, the proposed method integrates PINN-RLS-based online parameter correction, virtual-voltage-vector extension, disturbance-observer-based feedforward compensation, maximum-torque-per-ampere (MTPA) and quadratic-programming (QP) reference reconstruction, and deep-neural-network (DNN)-based torque-ripple compensation into the same closed-loop control framework. Unlike existing studies that usually optimize parameter identification, disturbance compensation, or ripple suppression separately, the proposed method emphasizes their coordinated interaction within the predictive control chain so as to simultaneously improve steady-state precision, disturbance rejection, and dynamic recovery performance. Simulation results show that the proposed HRPV-MPC achieves coordinated improvements in steady-state precision, dynamic response, and disturbance rejection under various operating conditions; compared with baseline FCS-MPC, it exhibits clear advantages in torque-ripple suppression, torque-error reduction, load-disturbance recovery, and speed-tracking performance, thereby validating the effectiveness and superiority of the constructed hierarchical collaborative framework. Full article

Aiming at the deterioration in Noise, Vibration and Harshness (NVH) performance caused by broadband torsional vibration in the integrated electric drive system (IEDS) of electric vehicles, most existing studies independently focus on electromagnetic excitation suppression or torsional vibration control of mechanical transmissions. Few researchers consider the coupling characteristics between the electromagnetic nonlinearity of motors and the nonlinearity of gear transmissions, making it difficult to realize the coordinated suppression of high- and low-frequency torsional vibration. In this paper, a seven-degree-of-freedom electromechanical coupling dynamic model is firstly established, which incorporates the electromagnetic torque ripple of the motor, the time-varying meshing stiffness of gears, meshing errors, and gear backlash nonlinearity. Through modal analysis and Campbell diagram solution, the natural characteristics and critical speed range of the system are clarified, and the generation mechanism of full-frequency band torsional vibration as well as the high–low frequency coupling characteristics are systematically revealed. On this basis, a coordinated active control strategy based on PD pole placement and harmonic current injection (PD-HCI) is proposed. The PD pole placement controller is adopted to suppress the low-frequency torsional vibration (0–20 Hz) of the transmission system, and the 5th/7th harmonic current injection is used to counteract the high-frequency torque ripple (above 200 Hz) of the motor, thereby achieving the coordinated suppression of broadband torsional vibration. The Matlab/Simulink R2023a simulation results show that the proposed control strategy reduces the torque fluctuation rate from 3.11% to 1.96%, the speed fluctuation rate from 0.10% to 0.03%, and the total harmonic distortion (THD) of stator current from 8.69% to 1.77% under steady-state operating conditions. Under transient operating conditions with sudden load changes, the stabilization time of fluctuations in speed and half-shaft torque is shortened by more than 80%, the impact amplitude is significantly reduced, and there is no loss in the vehicle’s dynamic response and speed tracking performance. Experimental results show that the coefficients of determination R2 of vehicle speed, motor speed, acceleration and torque are 0.9990, 0.9982, 0.9997 and 0.9997, respectively, which verifies the reliability of the established model. Full article

Inductive pulsed power remains attractive for demanding electromagnetic acceleration systems because of its high-current capability and rapid discharge capability. Within this class, XRAM is especially appealing because it combines series charging with parallel discharging of storage inductors. Under high-energy conditions, however, the conventional all-parallel XRAM topology suffers from concentrated blocking-voltage stress on the total output switch and limited effectiveness in transferring stored current to the representative railgun load considered in this work. To address these issues, this paper proposes a three-module grouped XRAM topology and examines its output behavior, parameter dependence, and commutation mechanism. Baseline comparison results show that the grouped arrangement establishes the load-driving path earlier and redistributes device stress more favorably. Its advantage is retained when both topologies are individually optimized with respect to the triggering threshold, indicating that the grouped topology offers a more effective route for high-current electromagnetic acceleration drive through earlier commutation establishment and more effective current transfer. Full article

This paper proposes a finite-time adaptive backstepping active suspension control strategy, integrating command filtering and composite learning, to address the degradation of ride comfort and attitude stability in heavy-duty vehicles caused by shifting loads and harsh roads. First, a nonlinear dynamic vehicle model is established, treating multi-source complex disturbances as a single lumped disturbance and accounting for suspension stiffness and damping nonlinearities. To stabilize the body attitude, a tri-axis controller governing the vertical, pitch, and roll motions is developed, incorporating the practical physical constraints of actuators. By employing a composite learning Radial Basis Function neural network, the controller achieves smooth approximation and precise compensation of lumped disturbances, significantly enhancing the system’s active disturbance rejection performance under complex excitations. Furthermore, the finite-time stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Finally, the strategy is evaluated under a 40% load mass mismatch and continuous random road excitations. Results indicate that the proposed strategy effectively curbs the deterioration of suspension nonlinearities during overloads, ensuring smoother dynamic transitions across all three axes. Compared to conventional backstepping control, the proposed approach reduces the root mean square values of vertical, pitch, and roll accelerations by 19%, 13%, and 35%, respectively. Ultimately, this framework effectively improves vehicle stability and disturbance rejection, providing a robust reference for heavy-duty vehicle chassis control. Full article

This study presents the development and validation of a simulation-calibrated kinematic formulation for a three-wheeled symmetric pipeline inspection robot operating under cylindrical confinement. The proposed model integrates analytical implementation in MATLAB 2023b with multibody simulation in SolidWorks 2023 to identify semi-empirical correction terms that improve motion prediction under straight and curved pipe conditions. The formulation incorporates curvature-dependent and asymmetry-related effects derived from structured simulation datasets, ensuring consistency between analytical predictions and simulated behavior within the evaluated operating range. Quantitative comparison using statistical indicators demonstrates strong agreement between both approaches, with MAE values of 0.0547 for linear velocity and 13.96 for displacement, RMSE values of 0.0681 and 19.0401, and coefficients of determination of $R^2=0.9997$ and $R^2=0.9476$, respectively. Slightly larger deviations are observed at higher rotational speeds. The results provide a consistent analytical representation of the robot’s motion under the studied geometric constraints and establish a basis for future experimental validation and control-oriented extensions in confined pipeline environments. Full article

In ceramic cup manufacturing, manual inspection is prone to missed detections and false positives, particularly for small surface defects. To address these challenges, this study presents an effective and efficient YOLOv11m-based detection framework, termed CEL-YOLOv11m, for precise identification of small-scale defects on ceramic surfaces. Specifically, a multi-scale convolution module (EMSC) is introduced to enhance the backbone feature extraction structure. By integrating convolution kernels of varying sizes, the module improves multi-scale feature representation, while grouped convolution is employed to reduce computational overhead. In the feature aggregation stage, a CRGseg-based structure is incorporated, and a refinement component (RCM) is designed to strengthen fine-grained information for small targets. Additionally, a cross-scale feature fusion strategy is applied to improve contextual representation across different resolutions. For the detection stage, a Layer-shared Detail-Enhanced Convolutional Detection Head (LSDECD) is adopted to improve fine-grained localization while improving computational efficiency through parameter sharing. Experiments conducted on a self-constructed ceramic defect dataset and the VisDrone2019 benchmark show that the proposed framework achieves competitive performance compared with representative methods. The model attains an mAP@50(%) of 54.8% with an inference speed of 89.9 FPS, providing a favorable trade-off between detection accuracy and computational efficiency while maintaining strong precision in small defect detection. Full article

To address the loss of closed-loop nozzle control caused by failures in the nozzle throat angle sensor or the nozzle oil separator valve displacement sensor, a fault-tolerant control method for the aeroengine nozzle actuator based on dynamic control loop reconfiguration is proposed. When either sensor fails, the control structure is reconfigured by removing the corresponding servo loop that can no longer form a closed loop, thereby preserving the remaining effective control loops and maintaining nozzle controllability. The proposed method is validated through full-digital simulation, hardware-in-the-loop simulation, and bench testing. The results show that the method ensures the stable operation of the digital control system over the representative operating conditions across the flight envelope and maintains satisfactory steady-state and dynamic performance under sensor fault conditions. The steady-state fluctuations before and after faults remain comparable. Although transient overshoot and droop increase after fault occurrence, the deterioration is limited: the fan speed overshoot and droop remain within 3% and 4%, respectively, while those of the compressor speed remain within 1% and 3%, respectively. Overall, the proposed method mitigates post-fault performance degradation and improves the fault tolerance of the nozzle control system. These results show that the method provides a feasible technical approach for enhancing the reliability of aeroengine digital control systems. Full article